@online{abc2018,
  title        = {Uber suspends self-driving car tests after vehicle hits and kills woman crossing the street in Arizona},
  author       = {{ABC News}},
  year         = 2018,
  url          = {http://www.abc.net.au/news/2018-03-20/uber-suspends-self-driving-car-tests-after-fatal-crash/9565586},
  date         = {2018-03-20}
}
@inproceedings{ahn2012bayesian,
  title        = {Bayesian posterior sampling via stochastic gradient fisher scoring},
  author       = {Ahn, Sungjin and Korattikara, Anoop and Welling, Max},
  year         = 2012,
  journal      = {arXiv preprint arXiv:1206.6380},
  booktitle    = {Proceedings of the 29th International Coference on International Conference on Machine Learning},
  pages        = {1771–1778}
}
@book{amari2012differential,
  title        = {Differential-geometrical methods in statistics},
  author       = {Amari, Shun-ichi},
  year         = 2012,
  publisher    = {Springer Science \& Business Media},
  volume       = 28
}
@article{antoran-2022-adapt-linear,
  title        = {Adapting the Linearised Laplace Model Evidence for Modern Deep Learning},
  author       = {Antor{\'a}n, Javier and Janz, David and Allingham, James Urquhart and Daxberger, Erik and Barbano, Riccardo and Nalisnick, Eric and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.08900v1},
  abstract     = {The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. The method provides reliable error bars and admits a closed-form expression for the model evidence, allowing for scalable selection of model hyperparameters. In this work, we examine the assumptions behind this method, particularly in conjunction with model selection. We show that these interact poorly with some now-standard tools of deep learning--stochastic approximation methods and normalisation layers--and make recommendations for how to better adapt this classic method to the modern setting. We provide theoretical support for our recommendations and validate them empirically on MLPs, classic CNNs, residual networks with and without normalisation layers, generative autoencoders and transformers.},
  archiveprefix = {arXiv},
  eprint       = {2206.08900},
  primaryclass = {stat.ML},
  file         = {~/org/pdfs/antoran-2022-adapt-linear.pdf}
}
@article{araujo2017classification,
  title        = {Classification of breast cancer histology images using convolutional neural networks},
  author       = {Ara{\'u}jo, Teresa and Aresta, Guilherme and Castro, Eduardo and Rouco, Jos{\'e} and Aguiar, Paulo and Eloy, Catarina and Pol{\'o}nia, Ant{\'o}nio and Campilho, Aur{\'e}lio},
  year         = 2017,
  journal      = {PloS one},
  publisher    = {Public Library of Science},
  volume       = 12,
  number       = 6,
  pages        = {e0177544}
}
@article{arrieta2020explainable,
  title        = {Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI},
  author       = {Arrieta, Alejandro Barredo and D{\'\i}az-Rodr{\'\i}guez, Natalia and Del Ser, Javier and Bennetot, Adrien and Tabik, Siham and Barbado, Alberto and Garc{\'\i}a, Salvador and Gil-L{\'o}pez, Sergio and Molina, Daniel and Benjamins, Richard and others},
  year         = 2020,
  journal      = {Information fusion},
  publisher    = {Elsevier},
  volume       = 58,
  pages        = {82--115}
}
@article{athey2015machine,
  title        = {Machine learning methods for estimating heterogeneous causal effects},
  author       = {Athey, Susan and Imbens, Guido W},
  year         = 2015,
  journal      = {stat},
  volume       = 1050,
  number       = 5
}
@article{badings-2022-probab-are,
  title        = {Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models With Epistemic Uncertainty},
  author       = {Badings, Thom and Romao, Licio and Abate, Alessandro and Jansen, Nils},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2210.05989v1},
  abstract     = {Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters and the presence of external disturbances lead to epistemic uncertainty. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. However, the underlying models exclusively capture aleatoric but not epistemic uncertainty, and thus require that model parameters and disturbances are known precisely. Our contribution to overcoming this restriction is a novel abstraction-based controller synthesis method for continuous-state models with stochastic noise, uncertain parameters, and external disturbances. By sampling techniques and robust analysis, we capture both aleatoric and epistemic uncertainty, with a user-specified confidence level, in the transition probability intervals of a so-called interval Markov decision process (iMDP). We then synthesize an optimal policy on this abstract iMDP, which translates (with the specified confidence level) to a feedback controller for the continuous model, with the same performance guarantees. Our experimental benchmarks confirm that accounting for epistemic uncertainty leads to controllers that are more robust against variations in parameter values.},
  archiveprefix = {arXiv},
  eprint       = {2210.05989},
  primaryclass = {eess.SY},
  file         = {/home/ethan/org/pdfs/badings-2022-probab-are.pdf}
}
@article{badrinarayanan2017segnet,
  title        = {Segnet: A deep convolutional encoder-decoder architecture for image segmentation},
  author       = {Badrinarayanan, Vijay and Kendall, Alex and Cipolla, Roberto},
  year         = 2017,
  journal      = {IEEE transactions on pattern analysis and machine intelligence},
  publisher    = {IEEE},
  volume       = 39,
  number       = 12,
  pages        = {2481--2495}
}
@article{bandeira2023free,
  title        = {On free energy barriers in Gaussian priors and failure of cold start MCMC for high-dimensional unimodal distributions},
  author       = {Bandeira, Afonso S.  and Maillard, Antoine  and Nickl, Richard  and Wang, Sven},
  year         = 2023,
  journal      = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  volume       = 381,
  number       = 2247,
  pages        = 20220150,
  doi          = {10.1098/rsta.2022.0150},
  url          = {https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2022.0150},
  eprint       = {https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2022.0150},
  abstract     = {We exhibit examples of high-dimensional unimodal posterior distributions arising in nonlinear regression models with Gaussian process priors for which Markov chain Monte Carlo (MCMC) methods can take an exponential run-time to enter the regions where the bulk of the posterior measure concentrates. Our results apply to worst-case initialized (`cold start') algorithms that are local in the sense that their step sizes cannot be too large on average. The counter-examples hold for general MCMC schemes based on gradient or random walk steps, and the theory is illustrated for Metropolis–Hastings adjusted methods such as preconditioned Crank–Nicolson and Metropolis-adjusted Langevin algorithm. This article is part of the theme issue `Bayesian inference: challenges, perspectives, and prospects'.}
}
@article{barber1998ensemble,
  title        = {Ensemble learning in Bayesian neural networks},
  author       = {Barber, David and Bishop, Christopher M},
  year         = 1998,
  journal      = {Nato ASI Series F Computer and Systems Sciences},
  publisher    = {Springer Verlag},
  volume       = 168,
  pages        = {215--238}
}
@inproceedings{barkan2021grad,
  title        = {Grad-sam: Explaining transformers via gradient self-attention maps},
  author       = {Barkan, Oren and Hauon, Edan and Caciularu, Avi and Katz, Ori and Malkiel, Itzik and Armstrong, Omri and Koenigstein, Noam},
  year         = 2021,
  booktitle    = {Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  pages        = {2882--2887}
}
@incollection{Bengio+chapter2007,
  title        = {Scaling Learning Algorithms Towards {AI}},
  author       = {Bengio, Yoshua and LeCun, Yann},
  year         = 2007,
  booktitle    = {Large Scale Kernel Machines},
  publisher    = {MIT Press}
}
@book{bernardo2000,
  title        = {Bayesian Theory},
  author       = {Jos\'{e} M. Bernardo and Adrian F. M. Smith},
  year         = 2000,
  publisher    = {John Wiley \& Sons}
}
@article{bertazzi2020adaptive,
  title        = {Adaptive Schemes for Piecewise Deterministic Monte Carlo Algorithms},
  author       = {Bertazzi, Andrea and Bierkens, Joris},
  year         = 2020,
  journal      = {arXiv preprint arXiv:2012.13924}
}
@inproceedings{betancourt2015fundamental,
  title        = {The fundamental incompatibility of scalable Hamiltonian Monte Carlo and naive data subsampling},
  author       = {Betancourt, Michael},
  year         = 2015,
  booktitle    = {International Conference on Machine Learning},
  location     = {Lille, France},
  publisher    = {JMLR.org},
  series       = {ICML'15},
  pages        = {533--540},
  numpages     = 8,
  acmid        = 3045176
}
@article{betancourt2017conceptual,
  title        = {A conceptual introduction to Hamiltonian Monte Carlo},
  author       = {Betancourt, Michael},
  year         = 2017,
  journal      = {arXiv preprint arXiv:1701.02434}
}
@article{betancourt2017geometric,
  title        = {The geometric foundations of hamiltonian monte carlo},
  author       = {Betancourt, Michael and Byrne, Simon and Livingstone, Sam and Girolami, Mark and others},
  year         = 2017,
  journal      = {Bernoulli},
  publisher    = {Bernoulli Society for Mathematical Statistics and Probability},
  volume       = 23,
  number       = {4a},
  pages        = {2257--2298}
}
@article{bierkens2016non,
  title        = {Non-reversible metropolis-hastings},
  author       = {Bierkens, Joris},
  year         = 2016,
  journal      = {Statistics and Computing},
  publisher    = {Springer},
  volume       = 26,
  number       = 6,
  pages        = {1213--1228}
}
@article{bierkens2016zig,
  title        = {{The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data}},
  author       = {{Bierkens}, Joris and {Fearnhead}, Paul and {Roberts}, Gareth},
  year         = 2016,
  month        = {Jul},
  journal      = {arXiv e-prints},
  pages        = {arXiv:1607.03188},
  keywords     = {Statistics - Computation, Mathematics - Probability, 65C60, 65C05, 62F15, 60J25},
  eid          = {arXiv:1607.03188},
  archiveprefix = {arXiv},
  eprint       = {1607.03188},
  primaryclass = {stat.CO},
  adsurl       = {https://ui.adsabs.harvard.edu/\#abs/2016arXiv160703188B},
  adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{bierkens2017limit,
  title        = {Limit theorems for the zig-zag process},
  author       = {Bierkens, Joris and Duncan, Andrew},
  year         = 2017,
  journal      = {Advances in Applied Probability},
  publisher    = {Cambridge University Press},
  volume       = 49,
  number       = 3,
  pages        = {791--825}
}
@article{bierkens2017piecewise,
  title        = {A piecewise deterministic scaling limit of lifted Metropolis--Hastings in the Curie--Weiss model},
  author       = {Bierkens, Joris and Roberts, Gareth and others},
  year         = 2017,
  journal      = {The Annals of Applied Probability},
  publisher    = {Institute of Mathematical Statistics},
  volume       = 27,
  number       = 2,
  pages        = {846--882}
}
@article{bierkens2019ergodicity,
  title        = {Ergodicity of the zigzag process},
  author       = {Bierkens, Joris and Roberts, Gareth O and Zitt, Pierre-Andr{\'e} and others},
  year         = 2019,
  journal      = {The Annals of Applied Probability},
  publisher    = {Institute of Mathematical Statistics},
  volume       = 29,
  number       = 4,
  pages        = {2266--2301}
}
@article{bierkens2019zig,
  title        = {The zig-zag process and super-efficient sampling for Bayesian analysis of big data},
  author       = {Bierkens, Joris and Fearnhead, Paul and Roberts, Gareth and others},
  year         = 2019,
  journal      = {The Annals of Statistics},
  publisher    = {Institute of Mathematical Statistics},
  volume       = 47,
  number       = 3,
  pages        = {1288--1320}
}
@article{bierkens2020boomerang,
  title        = {The Boomerang Sampler},
  author       = {Bierkens, Joris and Grazzi, Sebastiano and Kamatani, Kengo and Roberts, Gareth},
  year         = 2020,
  journal      = {arXiv preprint arXiv:2006.13777}
}
@book{bishop2006,
  title        = {Pattern recognition and machine learning},
  author       = {Bishop, C.},
  year         = 2006,
  publisher    = {Springer},
  address      = {New York}
}
@article{blei2017variational,
  title        = {Variational inference: A review for statisticians},
  author       = {Blei, David M and Kucukelbir, Alp and McAuliffe, Jon D},
  year         = 2017,
  journal      = {Journal of the American statistical Association},
  publisher    = {Taylor \& Francis},
  volume       = 112,
  number       = 518,
  pages        = {859--877}
}
@article{blundell2015weight,
  title        = {Weight uncertainty in neural networks},
  author       = {Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
  year         = 2015,
  journal      = {arXiv preprint arXiv:1505.05424}
}
@article{bortolussi-2022-robus-bayes,
  title        = {On the Robustness of Bayesian Neural Networks To Adversarial Attacks},
  author       = {Bortolussi, Luca and Carbone, Ginevra and Laurenti, Luca and Patane, Andrea and Sanguinetti, Guido and Wicker, Matthew},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2207.06154v1},
  abstract     = {Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial attacks is still an open problem. In this paper, we analyse the geometry of adversarial attacks in the large-data, overparameterized limit for Bayesian Neural Networks (BNNs). We show that, in the limit, vulnerability to gradient-based attacks arises as a result of degeneracy in the data distribution, i.e., when the data lies on a lower-dimensional submanifold of the ambient space. As a direct consequence, we demonstrate that in this limit BNN posteriors are robust to gradient-based adversarial attacks. Crucially, we prove that the expected gradient of the loss with respect to the BNN posterior distribution is vanishing, even when each neural network sampled from the posterior is vulnerable to gradient-based attacks. Experimental results on the MNIST, Fashion MNIST, and half moons datasets, representing the finite data regime, with BNNs trained with Hamiltonian Monte Carlo and Variational Inference, support this line of arguments, showing that BNNs can display both high accuracy on clean data and robustness to both gradient-based and gradient-free based adversarial attacks.},
  archiveprefix = {arXiv},
  eprint       = {2207.06154},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/bortolussi-2022-robus-bayes.pdf}
}
@article{bouchard2018bouncy,
  title        = {The bouncy particle sampler: A nonreversible rejection-free Markov chain Monte Carlo method},
  author       = {Bouchard-C{\^o}t{\'e}, Alexandre and Vollmer, Sebastian J and Doucet, Arnaud},
  year         = 2018,
  journal      = {Journal of the American Statistical Association},
  publisher    = {Taylor \& Francis},
  volume       = 113,
  number       = 522,
  pages        = {855--867}
}
@book{box2011bayesian,
  title        = {Bayesian inference in statistical analysis},
  author       = {Box, George EP and Tiao, George C},
  year         = 2011,
  publisher    = {John Wiley \& Sons}
}
@book{boyd2004convex,
  title        = {Convex optimization},
  author       = {Boyd, Stephen and Boyd, Stephen P and Vandenberghe, Lieven},
  year         = 2004,
  publisher    = {Cambridge university press},
  file         = {/home/ethan/org/pdfs/boyd2004convex.pdf}
}
@book{brooks2011handbook,
  title        = {Handbook of markov chain monte carlo},
  author       = {Brooks, Steve and Gelman, Andrew and Jones, Galin and Meng, Xiao-Li},
  year         = 2011,
  publisher    = {CRC press}
}
@inproceedings{brosse2018promises,
  title        = {The promises and pitfalls of stochastic gradient Langevin dynamics},
  author       = {Brosse, Nicolas and Durmus, Alain and Moulines, Eric},
  year         = 2018,
  booktitle    = {Advances in Neural Information Processing Systems},
  pages        = {8268--8278}
}
@article{brostow2009semantic,
  title        = {Semantic object classes in video: A high-definition ground truth database},
  author       = {Brostow, Gabriel J and Fauqueur, Julien and Cipolla, Roberto},
  year         = 2009,
  journal      = {Pattern Recognition Letters},
  publisher    = {Elsevier},
  volume       = 30,
  number       = 2,
  pages        = {88--97}
}
@inproceedings{buolamwini2018gender,
  title        = {Gender shades: Intersectional accuracy disparities in commercial gender classification},
  author       = {Buolamwini, Joy and Gebru, Timnit},
  year         = 2018,
  booktitle    = {Conference on fairness, accountability and transparency},
  pages        = {77--91}
}
@inproceedings{caesar2018coco,
  title        = {COCO-Stuff: Thing and Stuff Classes in Context},
  author       = {Caesar, Holger and Uijlings, Jasper and Ferrari, Vittorio},
  year         = 2018,
  month        = {June},
  booktitle    = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}
}
@article{campbell2019automated,
  title        = {Automated Scalable Bayesian Inference via Hilbert Coresets},
  author       = {Trevor Campbell and Tamara Broderick},
  year         = 2019,
  journal      = {Journal of Machine Learning Research},
  volume       = 20,
  number       = 15,
  pages        = {1--38},
  url          = {http://jmlr.org/papers/v20/17-613.html}
}
@article{caprio-2022-concen-inequal,
  title        = {Concentration Inequalities and Optimal Number of Layers for Stochastic Deep Neural Networks},
  author       = {Caprio, Michele and Mukherjee, Sayan},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.11241v1},
  abstract     = {We state concentration and martingale inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results allow us to introduce an expected classifier (EC), and to give probabilistic upper bound for the classification error of the EC. We also state the optimal number of layers for the SDNN via an optimal stopping procedure. We apply our analysis to a stochastic version of a feedforward neural network with ReLU activation function.},
  archiveprefix = {arXiv},
  eprint       = {2206.11241},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/caprio-2022-concen-inequal.pdf}
}
@inproceedings{caruana2015intelligible,
  title        = {Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission},
  author       = {Caruana, Rich and Lou, Yin and Gehrke, Johannes and Koch, Paul and Sturm, Marc and Elhadad, Noemie},
  year         = 2015,
  booktitle    = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages        = {1721--1730},
  organization = {Acm}
}
@article{chandra23_bayes_neural_networ_via_mcmc,
  title        = {Bayesian Neural Networks Via Mcmc: a Python-Based Tutorial},
  author       = {Chandra, Rohitash and Chen, Royce and Simmons, Joshua},
  year         = 2023,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2304.02595v1},
  abstract     = {Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling techniques are used to implement Bayesian inference. In the past three decades, MCMC methods have faced a number of challenges in being adapted to larger models (such as in deep learning) and big data problems. Advanced proposals that incorporate gradients, such as a Langevin proposal distribution, provide a means to address some of the limitations of MCMC sampling for Bayesian neural networks. Furthermore, MCMC methods have typically been constrained to use by statisticians and are still not prominent among deep learning researchers. We present a tutorial for MCMC methods that covers simple Bayesian linear and logistic models, and Bayesian neural networks. The aim of this tutorial is to bridge the gap between theory and implementation via coding, given a general sparsity of libraries and tutorials to this end. This tutorial provides code in Python with data and instructions that enable their use and extension. We provide results for some benchmark problems showing the strengths and weaknesses of implementing the respective Bayesian models via MCMC. We highlight the challenges in sampling multi-modal posterior distributions in particular for the case of Bayesian neural networks, and the need for further improvement of convergence diagnosis.},
  archiveprefix = {arXiv},
  eprint       = {2304.02595},
  primaryclass = {stat.ML},
  file         = {~/org/pdfs/chandra23\_bayes\_neural\_networ\_via\_mcmc.pdf}
}
@article{chen-2022-batch-ensem,
  title        = {Batch-Ensemble Stochastic Neural Networks for Out-Of-Distribution Detection},
  author       = {Chen, Xiongjie and Li, Yunpeng and Yang, Yongxin},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.12911v1},
  abstract     = {Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications. In this paper we propose an uncertainty quantification approach by modelling the distribution of features. We further incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble stochastic neural networks (BE-SNNs) and overcome the feature collapse problem. We compare the performance of the proposed BE-SNNs with the other state-of-the-art approaches and show that BE-SNNs yield superior performance on several OOD benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionMNIST vs NotMNIST dataset, and the CIFAR10 vs SVHN dataset.},
  archiveprefix = {arXiv},
  eprint       = {2206.12911},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/chen-2022-batch-ensem.pdf}
}
@inproceedings{chen1999lifting,
  title        = {Lifting Markov chains to speed up mixing},
  author       = {Chen, Fang and Lov{\'a}sz, L{\'a}szl{\'o} and Pak, Igor},
  year         = 1999,
  booktitle    = {Stoc'99},
  organization = {Citeseer}
}
@book{chen2012monte,
  title        = {Monte Carlo methods in Bayesian computation},
  author       = {Chen, Ming-Hui and Shao, Qi-Man and Ibrahim, Joseph G},
  year         = 2012,
  publisher    = {Springer Science \& Business Media}
}
@inproceedings{chen2014hmc,
  title        = {Stochastic Gradient Hamiltonian Monte Carlo},
  author       = {Tianqi Chen and Emily Fox and Carlos Guestrin},
  year         = 2014,
  month        = {22--24 Jun},
  booktitle    = {Proceedings of the 31st International Conference on Machine Learning},
  publisher    = {Pmlr},
  address      = {Bejing, China},
  series       = {Proceedings of Machine Learning Research},
  volume       = 32,
  number       = 2,
  pages        = {1683--1691},
  url          = {http://proceedings.mlr.press/v32/cheni14.html},
  editor       = {Eric P. Xing and Tony Jebara},
  pdf          = {http://proceedings.mlr.press/v32/cheni14.pdf},
  abstract     = {Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals.  The popularity of such methods has grown significantly in recent years.  However, a limitation of HMC methods is the required gradient computation for simulation of the Hamiltonian dynamical system-such computation is infeasible in problems involving a large sample size or streaming data. Instead, we must rely on a noisy gradient estimate computed from a subset of the data.  In this paper, we explore the properties of such a stochastic gradient HMC approach. Surprisingly, the natural implementation of the stochastic approximation can be arbitrarily bad.  To address this problem we introduce a variant that uses second-order Langevin dynamics with a friction term that counteracts the effects of the noisy gradient, maintaining the desired target distribution as the invariant distribution.  Results on simulated data validate our theory.  We also provide an application of our methods to a classification task using neural networks and to online Bayesian matrix factorization.},
  bibsource    = {dblp computer science bibliography, https://dblp.org},
  biburl       = {https://dblp.org/rec/conf/icml/ChenFG14.bib},
  timestamp    = {Wed, 29 May 2019 01:00:00 +0200}
}
@inproceedings{chen2014stochastic,
  title        = {Stochastic gradient hamiltonian monte carlo},
  author       = {Chen, Tianqi and Fox, Emily and Guestrin, Carlos},
  year         = 2014,
  booktitle    = {International conference on machine learning},
  pages        = {1683--1691}
}
@incollection{chen2015complete,
  title        = {A Complete Recipe for Stochastic Gradient MCMC},
  author       = {Ma, Yi-An and Chen, Tianqi and Fox, Emily},
  year         = 2015,
  booktitle    = {Advances in Neural Information Processing Systems 28},
  publisher    = {Curran Associates, Inc.},
  pages        = {2917--2925},
  url          = {http://papers.nips.cc/paper/5891-a-complete-recipe-for-stochastic-gradient-mcmc.pdf},
  editor       = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett}
}
@article{chen2015preconditioned,
  title        = {{Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks}},
  author       = {{Li}, C. and {Chen}, C. and {Carlson}, D. and {Carin}, L.},
  year         = 2015,
  month        = dec,
  journal      = {arXiv e-prints},
  archiveprefix = {arXiv},
  eprint       = {1512.07666},
  primaryclass = {stat.ML},
  keywords     = {Statistics - Machine Learning},
  adsurl       = {http://adsabs.harvard.edu/abs/2015arXiv151207666L},
  adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{chen2016thinning,
  title        = {Thinning algorithms for simulating point processes},
  author       = {Chen, Yuanda},
  year         = 2016,
  journal      = {Florida State University, Tallahassee, FL}
}
@article{chen2022vision,
  title        = {Vision transformer adapter for dense predictions},
  author       = {Chen, Zhe and Duan, Yuchen and Wang, Wenhai and He, Junjun and Lu, Tong and Dai, Jifeng and Qiao, Yu},
  year         = 2022,
  journal      = {arXiv preprint arXiv:2205.08534}
}
@misc{chollet2015,
  title        = {keras},
  author       = {Fran\c{c}ois Chollet},
  year         = 2015,
  journal      = {GitHub repository},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/fchollet/keras}},
  commit       = {5bcac37}
}
@article{claudiu2010,
  title        = {Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition},
  author       = {Dan Claudiu Ciresan and Ueli Meier and Luca Maria Gambardella and J{\"{u}}rgen Schmidhuber},
  year         = 2010,
  journal      = {CoRR},
  volume       = {abs/1003.0358},
  url          = {http://arxiv.org/abs/1003.0358},
  archiveprefix = {arXiv},
  eprint       = {1003.0358},
  timestamp    = {Mon, 13 Aug 2018 16:47:03 +0200},
  biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1003-0358},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{clevert2015fast,
  title        = {Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)},
  author       = {Djork{-}Arn{\'{e}} Clevert and Thomas Unterthiner and Sepp Hochreiter},
  year         = 2016,
  booktitle    = {4th International Conference on Learning Representations, {ICLR} 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings},
  url          = {http://arxiv.org/abs/1511.07289},
  editor       = {Yoshua Bengio and Yann LeCun},
  timestamp    = {Sat, 23 Jan 2021 01:12:05 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/ClevertUH15.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{cordts2016cityscapes,
  title        = {The cityscapes dataset for semantic urban scene understanding},
  author       = {Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
  year         = 2016,
  booktitle    = {Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages        = {3213--3223}
}
@article{Cortes1995,
  title        = {Support-vector networks},
  author       = {Cortes, Corinna and Vapnik, Vladimir},
  year         = 1995,
  month        = {Sep},
  day          = {01},
  journal      = {Machine Learning},
  volume       = 20,
  number       = 3,
  pages        = {273--297},
  doi          = {10.1007/bf00994018},
  issn         = {1573-0565},
  abstract     = {Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.}
}
@article{cremer2018inference,
  title        = {Inference Suboptimality in Variational Autoencoders},
  author       = {Chris Cremer and Xuechen Li and David K. Duvenaud},
  year         = 2018,
  journal      = {CoRR},
  volume       = {abs/1801.03558},
  url          = {http://arxiv.org/abs/1801.03558},
  archiveprefix = {arXiv},
  eprint       = {1801.03558},
  timestamp    = {Mon, 13 Aug 2018 16:46:01 +0200},
  biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1801-03558},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{cruz-2022-iterat-impor,
  title        = {Iterative Importance Sampling With Markov Chain Monte Carlo Sampling in Robust Bayesian Analysis},
  author       = {Cruz, Ivette Raices and Lindstr{\"o}m, Johan and Troffaes, Matthias C. M. and Sahlin, Ullrika},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.08728v1},
  abstract     = {Bayesian inference under a set of priors, called robust Bayesian analysis, allows for estimation of parameters within a model and quantification of epistemic uncertainty in quantities of interest by bounded (or imprecise) probability. Iterative importance sampling can be used to estimate bounds on the quantity of interest by optimizing over the set of priors. A method for iterative importance sampling when the robust Bayesian inference rely on Markov chain Monte Carlo (MCMC) sampling is proposed. To accommodate the MCMC sampling in iterative importance sampling, a new expression for the effective sample size of the importance sampling is derived, which accounts for the correlation in the MCMC samples. To illustrate the proposed method for robust Bayesian analysis, iterative importance sampling with MCMC sampling is applied to estimate the lower bound of the overall effect in a previously published meta-analysis with a random effects model. The performance of the method compared to a grid search method and under different degrees of prior-data conflict is also explored.},
  archiveprefix = {arXiv},
  eprint       = {2206.08728},
  primaryclass = {stat.CO},
  file         = {~/org/pdfs/cruz-2022-iterat-impor.pdf}
}
@article{cybenko1989approximation,
  title        = {Approximation by superpositions of a sigmoidal function},
  author       = {Cybenko, George},
  year         = 1989,
  journal      = {Mathematics of control, signals and systems},
  publisher    = {Springer},
  volume       = 2,
  number       = 4,
  pages        = {303--314}
}
@article{dahl2012context,
  title        = {Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition},
  author       = {Dahl, George E and Yu, Dong and Deng, Li and Acero, Alex},
  year         = 2012,
  journal      = {IEEE Transactions on audio, speech, and language processing},
  publisher    = {Ieee},
  volume       = 20,
  number       = 1,
  pages        = {30--42}
}
@inproceedings{damianou2011variational,
  title        = {Variational Gaussian process dynamical systems},
  author       = {Damianou, Andreas and Titsias, Michalis K and Lawrence, Neil D},
  year         = 2011,
  booktitle    = {Advances in Neural Information Processing Systems},
  pages        = {2510--2518}
}
@inproceedings{damianou2013deep,
  title        = {Deep gaussian processes},
  author       = {Damianou, Andreas and Lawrence, Neil},
  year         = 2013,
  booktitle    = {Artificial Intelligence and Statistics},
  pages        = {207--215}
}
@phdthesis{damianou2015deep,
  title        = {Deep Gaussian processes and variational propagation of uncertainty},
  author       = {Damianou, Andreas},
  year         = 2015,
  school       = {University of Sheffield}
}
@article{davis1984piecewise,
  title        = {Piecewise-deterministic Markov processes: a general class of non-diffusion stochastic models},
  author       = {Davis, Mark HA},
  year         = 1984,
  journal      = {Journal of the Royal Statistical Society: Series B (Methodological)},
  publisher    = {Wiley Online Library},
  volume       = 46,
  number       = 3,
  pages        = {353--376}
}
@book{davis1993markov,
  title        = {Markov models \& optimization},
  author       = {Davis, Mark HA},
  year         = 1993,
  publisher    = {CRC Press},
  volume       = 49
}
@article{dechesne2021bayesian,
  title        = {Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images},
  author       = {Dechesne, Cl\'{e}ment and Lassalle, Pierre and Lef\`{e}vre, S\'{e}bastien},
  year         = 2021,
  journal      = {Remote Sensing},
  volume       = 13,
  number       = 19,
  doi          = {10.3390/rs13193836},
  issn         = {2072-4292},
  url          = {https://www.mdpi.com/2072-4292/13/19/3836},
  article-number = 3836,
  abstract     = {In recent years, numerous deep learning techniques have been proposed to tackle the semantic segmentation of aerial and satellite images, increase trust in the leaderboards of main scientific contests and represent the current state-of-the-art. Nevertheless, despite their promising results, these state-of-the-art techniques are still unable to provide results with the level of accuracy sought in real applications, i.e., in operational settings. Thus, it is mandatory to qualify these segmentation results and estimate the uncertainty brought about by a deep network. In this work, we address uncertainty estimations in semantic segmentation. To do this, we relied on a Bayesian deep learning method, based on Monte Carlo Dropout, which allows us to derive uncertainty metrics along with the semantic segmentation. Built on the most widespread U-Net architecture, our model achieves semantic segmentation with high accuracy on several state-of-the-art datasets. More importantly, uncertainty maps are also derived from our model. While they allow for the performance of a sounder qualitative evaluation of the segmentation results, they also include valuable information to improve the reference databases.}
}
@book{degroot2005optimal,
  title        = {Optimal statistical decisions},
  author       = {DeGroot, Morris H},
  year         = 2005,
  publisher    = {John Wiley \& Sons}
}
@article{deligiannidis2019exponential,
  title        = {Exponential ergodicity of the bouncy particle sampler},
  author       = {Deligiannidis, George and Bouchard-C{\^o}t{\'e}, Alexandre and Doucet, Arnaud and others},
  year         = 2019,
  journal      = {The Annals of Statistics},
  publisher    = {Institute of Mathematical Statistics},
  volume       = 47,
  number       = 3,
  pages        = {1268--1287}
}
@inproceedings{deng2009imagenet,
  title        = {Imagenet: A large-scale hierarchical image database},
  author       = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  year         = 2009,
  booktitle    = {Computer Vision and Pattern Recognition},
  pages        = {248--255},
  organization = {Ieee}
}
@inproceedings{denker1991transforming,
  title        = {Transforming neural-net output levels to probability distributions},
  author       = {Denker, John S and Lecun, Yann},
  year         = 1991,
  booktitle    = {Advances in neural information processing systems},
  pages        = {853--859}
}
@article{denton2021geneology,
  title        = {On the genealogy of machine learning datasets: A critical history of ImageNet},
  author       = {Emily Denton and Alex Hanna and Razvan Amironesei and Andrew Smart and Hilary Nicole},
  year         = 2021,
  journal      = {Big Data \& Society},
  volume       = 8,
  number       = 2,
  pages        = 20539517211035955,
  doi          = {10.1177/20539517211035955},
  url          = {https://doi.org/10.1177/20539517211035955},
  eprint       = {https://doi.org/10.1177/20539517211035955},
  abstract     = {In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny. Many of these examinations have focused on the contents of machine learning datasets, finding glaring underrepresentation of minoritized groups. In contrast, relatively little work has been done to examine the norms, values, and assumptions embedded in these datasets. In this work, we conceptualize machine learning datasets as a type of informational infrastructure, and motivate a genealogy as method in examining the histories and modes of constitution at play in their creation. We present a critical history of ImageNet as an exemplar, utilizing critical discourse analysis of major texts around ImageNet's creation and impact. We find that assumptions around ImageNet and other large computer vision datasets more generally rely on three themes: the aggregation and accumulation of more data, the computational construction of meaning, and making certain types of data labor invisible. By tracing the discourses that surround this influential benchmark, we contribute to the ongoing development of the standards and norms around data development in machine learning and artificial intelligence research.}
}
@article{detommaso-2022-uncer-calib,
  title        = {Uncertainty Calibration in Bayesian Neural Networks Via Distance-Aware Priors},
  author       = {Detommaso, Gianluca and Gasparin, Alberto and Wilson, Andrew and Archambeau, Cedric},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2207.08200v1},
  abstract     = {As we move away from the data, the predictive uncertainty should increase, since a great variety of explanations are consistent with the little available information. We introduce Distance-Aware Prior (DAP) calibration, a method to correct overconfidence of Bayesian deep learning models outside of the training domain. We define DAPs as prior distributions over the model parameters that depend on the inputs through a measure of their distance from the training set. DAP calibration is agnostic to the posterior inference method, and it can be performed as a post-processing step. We demonstrate its effectiveness against several baselines in a variety of classification and regression problems, including benchmarks designed to test the quality of predictive distributions away from the data.},
  archiveprefix = {arXiv},
  eprint       = {2207.08200},
  primaryclass = {stat.ML},
  file         = {~/org/pdfs/detommaso-2022-uncer-calib.pdf}
}
@article{devroye2006nonuniform,
  title        = {Nonuniform random variate generation},
  author       = {Devroye, Luc},
  year         = 2006,
  journal      = {Handbooks in operations research and management science},
  publisher    = {Elsevier},
  volume       = 13,
  pages        = {83--121}
}
@article{diaconis2000analysis,
  title        = {Analysis of a nonreversible Markov chain sampler},
  author       = {Diaconis, Persi and Holmes, Susan and Neal, Radford M},
  year         = 2000,
  journal      = {Annals of Applied Probability},
  publisher    = {Jstor},
  pages        = {726--752}
}
@article{diaz2013existence,
  title        = {On the existence of a normal approximation to the distribution of the ratio of two independent normal random variables},
  author       = {D{\'\i}az-Franc{\'e}s, Elo{\'\i}sa and Rubio, Francisco J},
  year         = 2013,
  journal      = {Statistical Papers},
  publisher    = {Springer},
  volume       = 54,
  number       = 2,
  pages        = {309--323}
}
@article{dillon2017tensorflow,
  title        = {Tensorflow distributions},
  author       = {Dillon, Joshua V and Langmore, Ian and Tran, Dustin and Brevdo, Eugene and Vasudevan, Srinivas and Moore, Dave and Patton, Brian and Alemi, Alex and Hoffman, Matt and Saurous, Rif A},
  year         = 2017,
  journal      = {arXiv preprint arXiv:1711.10604},
  volume       = {abs/1711.10604},
  archiveprefix = {arXiv},
  eprint       = {1711.10604},
  timestamp    = {Mon, 13 Aug 2018 16:48:50 +0200},
  biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1711-10604},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{dinh2016density,
  title        = {Density estimation using Real {NVP}},
  author       = {Laurent Dinh and Jascha Sohl{-}Dickstein and Samy Bengio},
  year         = 2016,
  journal      = {CoRR},
  volume       = {abs/1605.08803},
  url          = {http://arxiv.org/abs/1605.08803},
  archiveprefix = {arXiv},
  eprint       = {1605.08803},
  timestamp    = {Mon, 13 Aug 2018 16:47:21 +0200},
  biburl       = {https://dblp.org/rec/bib/journals/corr/DinhSB16},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{dosovitskiy2020image,
  title        = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author       = {Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  year         = 2020,
  booktitle    = {International Conference on Learning Representations}
}
@article{draelos2020use,
  title        = {Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks},
  author       = {Draelos, Rachel Lea and Carin, Lawrence},
  year         = 2020,
  journal      = {arXiv e-prints},
  pages        = {arXiv--2011}
}
@article{duane1987hybrid,
  title        = {Hybrid monte carlo},
  author       = {Duane, Simon and Kennedy, Anthony D and Pendleton, Brian J and Roweth, Duncan},
  year         = 1987,
  journal      = {Physics letters B},
  publisher    = {Elsevier},
  volume       = 195,
  number       = 2,
  pages        = {216--222}
}
@article{dutordoir-2022-neural-diffus-proces,
  title        = {Neural Diffusion Processes},
  author       = {Dutordoir, Vincent and Saul, Alan and Ghahramani, Zoubin and Simpson, Fergus},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.03992v1},
  abstract     = {Gaussian processes provide an elegant framework for specifying prior and posterior distributions over functions. They are, however, also computationally expensive, and limited by the expressivity of their covariance function. We propose Neural Diffusion Processes (NDPs), a novel approach based upon diffusion models, that learn to sample from distributions over functions. Using a novel attention block, we can incorporate properties of stochastic processes, such as exchangeability, directly into the NDP's architecture. We empirically show that NDPs are able to capture functional distributions that are close to the true Bayesian posterior of a Gaussian process. This enables a variety of downstream tasks, including hyperparameter marginalisation and Bayesian optimisation.},
  archiveprefix = {arXiv},
  eprint       = {2206.03992},
  primaryclass = {stat.ML},
  file         = {~/org/pdfs/dutordoir-2022-neural-diffus-proces.pdf}
}
@article{ebrahimi-2019-uncer-guided,
  title        = {Uncertainty-Guided Continual Learning With Bayesian Neural Networks},
  author       = {Ebrahimi, Sayna and Elhoseiny, Mohamed and Darrell, Trevor and Rohrbach, Marcus},
  year         = 2019,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1906.02425v2},
  abstract     = {Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation to measure the parameters' \textit{importance}. In contrast, we propose Uncertainty-guided Continual Bayesian Neural Networks (UCB), where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in networks. Uncertainty is a natural way to identify \textit{what to remember} and \textit{what to change} as we continually learn, and thus mitigate catastrophic forgetting. We also show a variant of our model, which uses uncertainty for weight pruning and retains task performance after pruning by saving binary masks per tasks. We evaluate our UCB approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches. Additionally, we show that our model does not necessarily need task information at test time, i.e. it does not presume knowledge of which task a sample belongs to.},
  archiveprefix = {arXiv},
  eprint       = {1906.02425},
  primaryclass = {cs.LG},
  file         = {/home/ethan/org/pdfs/ebrahimi-2019-uncer-guided.pdf}
}
@article{erhan2009visualizing,
  title        = {Visualizing higher-layer features of a deep network},
  author       = {Erhan, Dumitru and Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
  year         = 2009,
  journal      = {University of Montreal},
  volume       = 1341,
  number       = 3,
  pages        = 1
}
@article{esteva2017dermatologist,
  title        = {Dermatologist-level classification of skin cancer with deep neural networks},
  author       = {Esteva, Andre and Kuprel, Brett and Novoa, Roberto A and Ko, Justin and Swetter, Susan M and Blau, Helen M and Thrun, Sebastian},
  year         = 2017,
  journal      = {Nature},
  publisher    = {Nature Publishing Group},
  volume       = 542,
  number       = 7639,
  pages        = 115
}
@misc{eu2016,
  title        = {Regulation (EU) 2016/679 of the European Parliment and of the Council},
  author       = {Council{\ }of{\ }European{\ }Union},
  year         = 2016
}
@article{farhani-2022-momen-dimin,
  title        = {Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks},
  author       = {Farhani, Ghazal and Kazachek, Alexander and Wang, Boyu},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.14862v1},
  abstract     = {Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs). However, they often fail to converge to desirable solutions when the target function contains high-frequency features, due to a phenomenon known as spectral bias. In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under stochastic gradient descent with momentum (SGDM). This demonstrates SGDM significantly reduces the effect of spectral bias. We have also examined why training a model via the Adam optimizer can accelerate the convergence while reducing the spectral bias. Moreover, our numerical experiments have confirmed that wide-enough networks using SGDM still converge to desirable solutions, even in the presence of high-frequency features. In fact, we show that the width of a network plays a critical role in convergence.},
  archiveprefix = {arXiv},
  eprint       = {2206.14862},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/farhani-2022-momen-dimin.pdf}
}
@article{fearnhead2016piecewise,
  title        = {{Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo}},
  author       = {{Fearnhead}, Paul and {Bierkens}, Joris and {Pollock}, Murray and {Roberts}, Gareth O},
  year         = 2016,
  month        = {Nov},
  journal      = {arXiv e-prints},
  pages        = {arXiv:1611.07873},
  keywords     = {Statistics - Computation, Mathematics - Statistics Theory, Statistics - Methodology, Statistics - Machine Learning},
  eid          = {arXiv:1611.07873},
  archiveprefix = {arXiv},
  eprint       = {1611.07873},
  primaryclass = {stat.CO},
  adsurl       = {https://ui.adsabs.harvard.edu/\#abs/2016arXiv161107873F},
  adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{fearnhead2018piecewise,
  title        = {Piecewise deterministic Markov processes for continuous-time Monte Carlo},
  author       = {Fearnhead, Paul and Bierkens, Joris and Pollock, Murray and Roberts, Gareth O and others},
  year         = 2018,
  journal      = {Statistical Science},
  publisher    = {Institute of Mathematical Statistics},
  volume       = 33,
  number       = 3,
  pages        = {386--412}
}
@article{fei2007learning,
  title        = {Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories},
  author       = {Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
  year         = 2007,
  journal      = {Computer vision and Image understanding},
  publisher    = {Elsevier},
  volume       = 106,
  number       = 1,
  pages        = {59--70}
}
@article{feng2020deep,
  title        = {Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges},
  author       = {Feng, Di and Haase-Sch{\"u}tz, Christian and Rosenbaum, Lars and Hertlein, Heinz and Glaeser, Claudius and Timm, Fabian and Wiesbeck, Werner and Dietmayer, Klaus},
  year         = 2020,
  journal      = {IEEE Transactions on Intelligent Transportation Systems},
  publisher    = {IEEE},
  volume       = 22,
  number       = 3,
  pages        = {1341--1360}
}
@inproceedings{fleuret2021uncertainty,
  title        = {Uncertainty reduction for model adaptation in semantic segmentation},
  author       = {Fleuret, Francois and others},
  year         = 2021,
  booktitle    = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages        = {9613--9623}
}
@misc{freidank19:_arspy,
  title        = {ARSpy},
  author       = {Moritz Freidank and Matt Graham},
  year         = 2019,
  url          = {https://github.com/MFreidank/ARSpy}
}
@article{frey1999variational,
  title        = {Variational learning in nonlinear Gaussian belief networks},
  author       = {Frey, Brendan J and Hinton, Geoffrey E},
  year         = 1999,
  journal      = {Neural Computation},
  publisher    = {MIT Press},
  volume       = 11,
  number       = 1,
  pages        = {193--213}
}
@article{fu2020scene,
  title        = {Scene segmentation with dual relation-aware attention network},
  author       = {Fu, Jun and Liu, Jing and Jiang, Jie and Li, Yong and Bao, Yongjun and Lu, Hanqing},
  year         = 2020,
  journal      = {IEEE Transactions on Neural Networks and Learning Systems},
  publisher    = {IEEE},
  volume       = 32,
  number       = 6,
  pages        = {2547--2560}
}
@article{funahashi1989approximate,
  title        = {On the approximate realization of continuous mappings by neural networks},
  author       = {Funahashi, Ken-Ichi},
  year         = 1989,
  journal      = {Neural networks},
  publisher    = {Pergamon},
  volume       = 2,
  number       = 3,
  pages        = {183--192}
}
@article{gal2015dropoutAppendix,
  title        = {Dropout as a Bayesian Approximation: Appendix},
  author       = {Gal, Yarin and Ghahramani, Zoubin},
  year         = 2015,
  journal      = {arXiv preprint arXiv:1506.02157},
  eprint       = {1506.02157},
  archiveprefix = {arXiv}
}
@inproceedings{gal2015dropoutslides,
  title        = {Dropout as a Bayesian approximation: Insights and applications},
  author       = {Gal, Yarin and Ghahramani, Zoubin},
  year         = 2015,
  booktitle    = {Deep Learning Workshop, ICML},
  volume       = 1,
  pages        = 2
}
@inproceedings{gal2016dropout,
  title        = {Dropout as a Bayesian approximation: Representing model uncertainty in deep learning},
  author       = {Gal, Yarin and Ghahramani, Zoubin},
  year         = 2016,
  booktitle    = {international conference on machine learning},
  publisher    = {JMLR.org},
  series       = {{JMLR} Workshop and Conference Proceedings},
  volume       = 48,
  pages        = {1050--1059},
  url          = {http://proceedings.mlr.press/v48/gal16.html},
  bibsource    = {dblp computer science bibliography, https://dblp.org},
  biburl       = {https://dblp.org/rec/conf/icml/GalG16.bib},
  editor       = {Maria{-}Florina Balcan and Kilian Q. Weinberger},
  timestamp    = {Wed, 29 May 2019 01:00:00 +0200}
}
@article{gal2016uncertainty,
  title        = {Uncertainty in deep learning},
  author       = {Gal, Yarin},
  year         = 2016,
  journal      = {University of Cambridge}
}
@inproceedings{gal2017concrete,
  title        = {Concrete dropout},
  author       = {Gal, Yarin and Hron, Jiri and Kendall, Alex},
  year         = 2017,
  booktitle    = {Advances in Neural Information Processing Systems},
  pages        = {3581--3590}
}
@article{garcia-garcia-2017-review-deep,
  title        = {A Review on Deep Learning Techniques Applied To Semantic Segmentation},
  author       = {Garcia-Garcia, Alberto and Orts-Escolano, Sergio and Oprea, Sergiu and Villena-Martinez, Victor and Garcia-Rodriguez, Jose},
  year         = 2017,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1704.06857v1},
  abstract     = {Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.},
  archiveprefix = {arXiv},
  eprint       = {1704.06857},
  primaryclass = {cs.CV},
  file         = {/home/ethan/org/pdfs/garcia-garcia-2017-review-deep.pdf}
}
@article{garriga2018deep,
  title        = {Deep convolutional networks as shallow gaussian processes},
  author       = {Garriga-Alonso, Adri{\`a} and Aitchison, Laurence and Rasmussen, Carl Edward},
  year         = 2018,
  journal      = {arXiv preprint arXiv:1808.05587}
}
@article{gast2018light,
  title        = {Lightweight Probabilistic Deep Networks},
  author       = {Gast, Jochen and Roth, Stefan},
  year         = 2018,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1805.11327v1},
  abstract     = {Even though probabilistic treatments of neural networks have a long history, they have not found widespread use in practice. Sampling approaches are often too slow already for simple networks. The size of the inputs and the depth of typical CNN architectures in computer vision only compound this problem.  Uncertainty in neural networks has thus been largely ignored in practice, despite the fact that it may provide important information about the reliability of predictions and the inner workings of the network. In this paper, we introduce two lightweight approaches to making supervised learning with probabilistic deep networks practical: First, we suggest probabilistic output layers for classification and regression that require only minimal changes to existing networks. Second, we employ assumed density filtering and show that activation uncertainties can be propagated in a practical fashion through the entire network, again with minor changes. Both probabilistic networks retain the predictive power of the deterministic counterpart, but yield uncertainties that correlate well with the empirical error induced by their predictions. Moreover, the robustness to adversarial examples is significantly increased.},
  archiveprefix = {arXiv},
  eprint       = {1805.11327},
  primaryclass = {cs.CV}
}
@article{gilks1992adaptive,
  title        = {Adaptive rejection sampling for Gibbs sampling},
  author       = {Gilks, Walter R and Wild, Pascal},
  year         = 1992,
  journal      = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
  publisher    = {Wiley Online Library},
  volume       = 41,
  number       = 2,
  pages        = {337--348}
}
@article{gilks1995adaptive,
  title        = {Adaptive rejection Metropolis sampling within Gibbs sampling},
  author       = {Gilks, Wally R and Best, Nicky G and Tan, KKC},
  year         = 1995,
  journal      = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
  publisher    = {Wiley Online Library},
  volume       = 44,
  number       = 4,
  pages        = {455--472}
}
@article{giordana2018covariances,
  title        = {Covariances, Robustness, and Variational Bayes},
  author       = {Ryan Giordano and Tamara Broderick and Michael I. Jordan},
  year         = 2018,
  journal      = {Journal of Machine Learning Research},
  volume       = 19,
  number       = 51,
  pages        = {1--49},
  url          = {http://jmlr.org/papers/v19/17-670.html}
}
@article{girolami2011riemann,
  title        = {Riemann manifold langevin and hamiltonian monte carlo methods},
  author       = {Girolami, Mark and Calderhead, Ben},
  year         = 2011,
  journal      = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  publisher    = {Wiley Online Library},
  volume       = 73,
  number       = 2,
  pages        = {123--214}
}
@inproceedings{girshick2014rich,
  title        = {Rich feature hierarchies for accurate object detection and semantic segmentation},
  author       = {Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},
  year         = 2014,
  booktitle    = {Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages        = {580--587}
}
@inproceedings{glorot2011deep,
  title        = {Deep sparse rectifier neural networks},
  author       = {Glorot, Xavier and Bordes, Antoine and Bengio, Yoshua},
  year         = 2011,
  booktitle    = {Aistats},
  pages        = {315--323}
}
@incollection{goan2019bayesian,
  title        = {Bayesian Neural Networks: an Introduction and Review},
  author       = {Goan, E.},
  year         = {in preparation},
  booktitle    = {Studies in Bayesian Modelling and Analysis of Big Data},
  publisher    = {Springer},
  editor       = {Mengersen, K. and Pudlo, P. and Robert. C}
}
@incollection{goan2020bayesian,
  title        = {Bayesian Neural Networks: an Introduction and Review},
  author       = {Goan, E.},
  year         = 2020,
  booktitle    = {Studies in Bayesian Modelling and Analysis of Big Data},
  publisher    = {Springer},
  editor       = {Mengersen, K. and Pudlo, P. and Robert. C}
}
@inproceedings{goodfellow2013maxout,
  title        = {Maxout networks},
  author       = {Goodfellow, Ian and Warde-Farley, David and Mirza, Mehdi and Courville, Aaron and Bengio, Yoshua},
  year         = 2013,
  booktitle    = {International conference on machine learning},
  pages        = {1319--1327},
  organization = {Pmlr}
}
@book{goodfellow2016deep,
  title        = {Deep learning},
  author       = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
  year         = 2016,
  publisher    = {MIT Press},
  volume       = 1
}
@article{goodman2016european,
  title        = {European Union regulations on algorithmic decision-making and a" right to explanation"},
  author       = {Goodman, Bryce and Flaxman, Seth},
  year         = 2016,
  journal      = {AI magazine},
  volume       = 38,
  number       = 3,
  pages        = {50--57},
  eprint       = {1606.08813},
  archiveprefix = {arXiv}
}
@article{GPflow2017,
  title        = {{{GP}flow: A {G}aussian process library using {T}ensor{F}low}},
  author       = {Matthews, Alexander G. de G. and {van der Wilk}, Mark and Nickson, Tom and Fujii, Keisuke. and {Boukouvalas}, Alexis and {Le{\'o}n-Villagr{\'a}}, Pablo and Ghahramani, Zoubin and Hensman, James},
  year         = 2017,
  month        = 4,
  journal      = {Journal of Machine Learning Research},
  volume       = 18,
  number       = 40,
  pages        = {1--6},
  url          = {http://jmlr.org/papers/v18/16-537.html}
}
@article{grant2009lit,
  title        = {A typology of reviews: an analysis of 14 review types and associated methodologies},
  author       = {Maria J. Grant and Andrew Booth},
  journal      = {Health Information \& Libraries Journal},
  volume       = 26,
  number       = 2,
  pages        = {91--108},
  doi          = {10.1111/j.1471-1842.2009.00848.x},
  abstract     = {Abstract Background and objectives:\hspace{0.6em} The expansion of evidence-based practice across sectors has lead to an increasing variety of review types. However, the diversity of terminology used means that the full potential of these review types may be lost amongst a confusion of indistinct and misapplied terms. The objective of this study is to provide descriptive insight into the most common types of reviews, with illustrative examples from health and health information domains. Methods:\hspace{0.6em} Following scoping searches, an examination was made of the vocabulary associated with the literature of review and synthesis (literary warrant). A simple analytical framework--Search, AppraisaL, Synthesis and Analysis (SALSA)--was used to examine the main review types. Results:\hspace{0.6em} Fourteen review types and associated methodologies were analysed against the SALSA framework, illustrating the inputs and processes of each review type. A description of the key characteristics is given, together with perceived strengths and weaknesses. A limited number of review types are currently utilized within the health information domain. Conclusions:\hspace{0.6em} Few review types possess prescribed and explicit methodologies and many fall short of being mutually exclusive. Notwithstanding such limitations, this typology provides a valuable reference point for those commissioning, conducting, supporting or interpreting reviews, both within health information and the wider health care domain.}
}
@inproceedings{grathwohl2019your,
  title        = {Your classifier is secretly an energy based model and you should treat it like one},
  author       = {Grathwohl, Will and Wang, Kuan-Chieh and Jacobsen, Joern-Henrik and Duvenaud, David and Norouzi, Mohammad and Swersky, Kevin},
  year         = 2019,
  booktitle    = {International Conference on Learning Representations}
}
@incollection{graves2011,
  title        = {Practical Variational Inference for Neural Networks},
  author       = {Graves, Alex},
  year         = 2011,
  booktitle    = {Advances in Neural Information Processing Systems 24},
  publisher    = {Curran Associates, Inc.},
  pages        = {2348--2356},
  editor       = {J. Shawe-Taylor and R. S. Zemel and P. L. Bartlett and F. Pereira and K. Q. Weinberger}
}
@inproceedings{graves2011practical,
  title        = {Practical Variational Inference for Neural Networks},
  author       = {Alex Graves},
  year         = 2011,
  booktitle    = {Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain},
  pages        = {2348--2356},
  url          = {https://proceedings.neurips.cc/paper/2011/hash/7eb3c8be3d411e8ebfab08eba5f49632-Abstract.html},
  bibsource    = {dblp computer science bibliography, https://dblp.org},
  biburl       = {https://dblp.org/rec/conf/nips/Graves11.bib},
  editor       = {John Shawe{-}Taylor and Richard S. Zemel and Peter L. Bartlett and Fernando C. N. Pereira and Kilian Q. Weinberger},
  timestamp    = {Thu, 21 Jan 2021 00:00:00 +0100}
}
@report{griffin2007caltech,
  title        = {Caltech-256 object category dataset},
  author       = {Griffin, Gregory and Holub, Alex and Perona, Pietro},
  year         = 2007,
  type         = {Technical Report 7694},
  institution  = {California Institute of Technology}
}
@article{gull1999quantified,
  title        = {Quantified Maximum Entropy MemSys5 Users' Manual},
  author       = {Gull, Stephen F and Skilling, John},
  year         = 1991,
  journal      = {Maximum Entropy Data Consultants Ltd},
  volume       = 33
}
@article{gunning2017explainable,
  title        = {Explainable artificial intelligence (xai)},
  author       = {Gunning, David},
  year         = 2017,
  journal      = {Defense Advanced Research Projects Agency (DARPA), nd Web}
}
@article{guo-2022-survey-uncer,
  title        = {A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning},
  author       = {Guo, Zhen and Wan, Zelin and Zhang, Qisheng and Zhao, Xujiang and Chen, Feng and Cho, Jin-Hee and Zhang, Qi and Kaplan, Lance M. and Jeong, Dong H. and J{\o}sang, Audun},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.05675v2},
  abstract     = {An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different types of uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artificial Intelligence (AI) domains. In particular, belief/evidence theories have been studied in KRR since the 1960s to reason and measure uncertainties to enhance decision-making effectiveness. We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty. In this survey paper, we discuss several popular belief theories and their core ideas dealing with uncertainty causes and types and quantifying them, along with the discussions of their applicability in ML/DL. In addition, we discuss three main approaches that leverage belief theories in Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough DNNs, in terms of their uncertainty causes, types, and quantification methods along with their applicability in diverse problem domains. Based on our in-depth survey, we discuss insights, lessons learned, limitations of the current state-of-the-art bridging belief theories and ML/DL, and finally, future research directions.},
  archiveprefix = {arXiv},
  eprint       = {2206.05675},
  primaryclass = {cs.AI},
  file         = {~/org/pdfs/guo-2022-survey-uncer.pdf}
}
@inproceedings{guo2017calibration,
  title        = {On calibration of modern neural networks},
  author       = {Guo, Chuan and Pleiss, Geoff and Sun, Yu and Weinberger, Kilian Q},
  year         = 2017,
  booktitle    = {International conference on machine learning},
  pages        = {1321--1330},
  organization = {PMLR}
}
@online{Guynn2015,
  title        = {Google Photos labeled black people 'gorillas'},
  author       = {Guynn, Jessica},
  url          = {https://www.usatoday.com/story/tech/2015/07/01/google-apologizes-after-photos-identify-black-people-as-gorillas/29567465/},
  date         = {2015-07-01},
  editor       = {{USA Today}}
}
@article{ha-2022-uncer-aware,
  title        = {Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems Via Bayesian Neural Networks},
  author       = {Ha, Huong and Fan, Zongwen and Zhang, Hongyu},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2212.13359v1},
  abstract     = {Configurable software systems are employed in many important application domains. Understanding the performance of the systems under all configurations is critical to prevent potential performance issues caused by misconfiguration. However, as the number of configurations can be prohibitively large, it is not possible to measure the system performance under all configurations. Thus, a common approach is to build a prediction model from a limited measurement data to predict the performance of all configurations as scalar values. However, it has been pointed out that there are different sources of uncertainty coming from the data collection or the modeling process, which can make the scalar predictions not certainly accurate. To address this problem, we propose a Bayesian deep learning based method, namely BDLPerf, that can incorporate uncertainty into the prediction model. BDLPerf can provide both scalar predictions for configurations' performance and the corresponding confidence intervals of these scalar predictions. We also develop a novel uncertainty calibration technique to ensure the reliability of the confidence intervals generated by a Bayesian prediction model. Finally, we suggest an efficient hyperparameter tuning technique so as to train the prediction model within a reasonable amount of time whilst achieving high accuracy. Our experimental results on 10 real-world systems show that BDLPerf achieves higher accuracy than existing approaches, in both scalar performance prediction and confidence interval estimation.},
  archiveprefix = {arXiv},
  eprint       = {2212.13359},
  primaryclass = {cs.SE},
  file         = {/home/ethan/org/pdfs/ha-2022-uncer-aware.pdf}
}
@article{hafner2018reliable,
  title        = {Reliable uncertainty estimates in deep neural networks using noise contrastive priors},
  author       = {Hafner, Danijar and Tran, Dustin and Lillicrap, Timothy and Irpan, Alex and Davidson, James},
  year         = 2018,
  journal      = {arXiv preprint arXiv:1807.09289}
}
@article{han-2022-scalab-mcmc,
  title        = {Scalable Mcmc Sampling for Nonsymmetric Determinantal Point Processes},
  author       = {Han, Insu and Gartrell, Mike and Dohmatob, Elvis and Karbasi, Amin},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2207.00486v1},
  abstract     = {A determinantal point process (DPP) is an elegant model that assigns a probability to every subset of a collection of $n$ items. While conventionally a DPP is parameterized by a symmetric kernel matrix, removing this symmetry constraint, resulting in nonsymmetric DPPs (NDPPs), leads to significant improvements in modeling power and predictive performance. Recent work has studied an approximate Markov chain Monte Carlo (MCMC) sampling algorithm for NDPPs restricted to size-\$k\$ subsets (called \$k\$-NDPPs). However, the runtime of this approach is quadratic in \$n\$, making it infeasible for large-scale settings. In this work, we develop a scalable MCMC sampling algorithm for \$k\$-NDPPs with low-rank kernels, thus enabling runtime that is sublinear in \$n\$. Our method is based on a state-of-the-art NDPP rejection sampling algorithm, which we enhance with a novel approach for efficiently constructing the proposal distribution. Furthermore, we extend our scalable \$k\$-NDPP sampling algorithm to NDPPs without size constraints. Our resulting sampling method has polynomial time complexity in the rank of the kernel, while the existing approach has runtime that is exponential in the rank. With both a theoretical analysis and experiments on real-world datasets, we verify that our scalable approximate sampling algorithms are orders of magnitude faster than existing sampling approaches for \$k\$-NDPPs and NDPPs.},
  archiveprefix = {arXiv},
  eprint       = {2207.00486},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/han-2022-scalab-mcmc.pdf}
}
@inproceedings{he2016deep,
  title        = {Deep residual learning for image recognition},
  author       = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  year         = 2016,
  booktitle    = {Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages        = {770--778}
}
@article{heek2019bayesian,
  title        = {Bayesian inference for large scale image classification},
  author       = {Heek, Jonathan and Kalchbrenner, Nal},
  year         = 2019,
  journal      = {arXiv preprint arXiv:1908.03491}
}
@article{hendrycks-2016-gauss-error,
  title        = {Gaussian Error Linear Units (GELUs)},
  author       = {Hendrycks, Dan and Gimpel, Kevin},
  year         = 2016,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1606.08415v4},
  abstract     = {We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\Phi(x)$, where \$\Phi(x)\$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs (\$x\mathbf{1}\_{x>0}\$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks.},
  archiveprefix = {arXiv},
  eprint       = {1606.08415},
  primaryclass = {cs.LG}
}
@inproceedings{hernandez2015probabilistic,
  title        = {Probabilistic backpropagation for scalable learning of bayesian neural networks},
  author       = {Hern{\'a}ndez-Lobato, Jos{\'e} Miguel and Adams, Ryan},
  year         = 2015,
  booktitle    = {International Conference on Machine Learning},
  pages        = {1861--1869}
}
@article{Hinton06,
  title        = {A Fast Learning Algorithm for Deep Belief Nets},
  author       = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye},
  year         = 2006,
  journal      = {Neural Computation},
  volume       = 18,
  pages        = {1527--1554}
}
@inproceedings{hinton1993keeping,
  title        = {Keeping the neural networks simple by minimizing the description length of the weights},
  author       = {Hinton, Geoffrey E and Van Camp, Drew},
  year         = 1993,
  booktitle    = {Proceedings of the sixth annual conference on Computational learning theory},
  pages        = {5--13},
  organization = {ACM}
}
@article{hinton2006fast,
  title        = {A fast learning algorithm for deep belief nets},
  author       = {Hinton, Geoffrey E and Osindero, Simon and Teh, Yee-Whye},
  year         = 2006,
  journal      = {Neural computation},
  publisher    = {MIT Press},
  volume       = 18,
  number       = 7,
  pages        = {1527--1554}
}
@article{hinton2012deep,
  title        = {Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups},
  author       = {Hinton, Geoffrey and Deng, Li and Yu, Dong and Dahl, George E and Mohamed, Abdel-rahman and Jaitly, Navdeep and Senior, Andrew and Vanhoucke, Vincent and Nguyen, Patrick and Sainath, Tara N and others},
  year         = 2012,
  journal      = {IEEE Signal Processing Magazine},
  publisher    = {Ieee},
  volume       = 29,
  number       = 6,
  pages        = {82--97}
}
@article{hoffman2013stochastic,
  title        = {Stochastic variational inference},
  author       = {Hoffman, Matthew D and Blei, David M and Wang, Chong and Paisley, John},
  year         = 2013,
  journal      = {The Journal of Machine Learning Research},
  publisher    = {JMLR. org},
  volume       = 14,
  number       = 1,
  pages        = {1303--1347}
}
@article{hoffman2014no,
  title        = {The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.},
  author       = {Hoffman, Matthew D and Gelman, Andrew and others},
  year         = 2014,
  journal      = {J. Mach. Learn. Res.},
  volume       = 15,
  number       = 1,
  pages        = {1593--1623}
}
@article{holland1985statistics,
  title        = {Statistics and causal inference},
  author       = {Holland, Paul W and Glymour, Clark and Granger, Clive},
  year         = 1985,
  journal      = {ETS Research Report Series},
  publisher    = {Wiley Online Library},
  volume       = 1985,
  number       = 2
}
@misc{holzinger2017we,
  title        = {What do we need to build explainable AI systems for the medical domain?},
  author       = {Holzinger, Andreas and Biemann, Chris and Pattichis, Constantinos S and Kell, Douglas B},
  year         = 2017,
  journal      = {arXiv preprint arXiv:1712.09923}
}
@article{hornik1991approximation,
  title        = {Approximation capabilities of multilayer feedforward networks},
  author       = {Hornik, Kurt},
  year         = 1991,
  journal      = {Neural networks},
  publisher    = {Elsevier},
  volume       = 4,
  number       = 2,
  pages        = {251--257}
}
@inproceedings{hou2021coordinate,
  title        = {Coordinate attention for efficient mobile network design},
  author       = {Hou, Qibin and Zhou, Daquan and Feng, Jiashi},
  year         = 2021,
  booktitle    = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages        = {13713--13722}
}
@article{hron-2022-wide-bayes,
  title        = {Wide Bayesian Neural Networks Have a Simple Weight Posterior: Theory and Accelerated Sampling},
  author       = {Hron, Jiri and Novak, Roman and Pennington, Jeffrey and Sohl-Dickstein, Jascha},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.07673v1},
  abstract     = {We introduce repriorisation, a data-dependent reparameterisation which transforms a Bayesian neural network (BNN) posterior to a distribution whose KL divergence to the BNN prior vanishes as layer widths grow. The repriorisation map acts directly on parameters, and its analytic simplicity complements the known neural network Gaussian process (NNGP) behaviour of wide BNNs in function space. Exploiting the repriorisation, we develop a Markov chain Monte Carlo (MCMC) posterior sampling algorithm which mixes faster the wider the BNN. This contrasts with the typically poor performance of MCMC in high dimensions. We observe up to 50x higher effective sample size relative to no reparametrisation for both fully-connected and residual networks. Improvements are achieved at all widths, with the margin between reparametrised and standard BNNs growing with layer width.},
  archiveprefix = {arXiv},
  eprint       = {2206.07673},
  primaryclass = {stat.ML}
}
@inproceedings{huang2018efficient,
  title        = {Efficient uncertainty estimation for semantic segmentation in videos},
  author       = {Huang, Po-Yu and Hsu, Wan-Ting and Chiu, Chun-Yueh and Wu, Ting-Fan and Sun, Min},
  year         = 2018,
  booktitle    = {Proceedings of the European Conference on Computer Vision (ECCV)},
  pages        = {520--535}
}
@incollection{huggins2016core,
  title        = {Coresets for Scalable Bayesian Logistic Regression},
  author       = {Huggins, Jonathan and Campbell, Trevor and Broderick, Tamara},
  year         = 2016,
  booktitle    = {Advances in Neural Information Processing Systems 29},
  publisher    = {Curran Associates, Inc.},
  pages        = {4080--4088},
  url          = {http://papers.nips.cc/paper/6486-coresets-for-scalable-bayesian-logistic-regression.pdf},
  editor       = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett}
}
@article{izmailov-2022-featur-learn,
  title        = {On Feature Learning in the Presence of Spurious Correlations},
  author       = {Izmailov, Pavel and Kirichenko, Polina and Gruver, Nate and Wilson, Andrew Gordon},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2210.11369v1},
  abstract     = {Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds. In this paper we evaluate the amount of information about the core (non-spurious) features that can be decoded from the representations learned by standard empirical risk minimization (ERM) and specialized group robustness training. Following recent work on Deep Feature Reweighting (DFR), we evaluate the feature representations by re-training the last layer of the model on a held-out set where the spurious correlation is broken. On multiple vision and NLP problems, we show that the features learned by simple ERM are highly competitive with the features learned by specialized group robustness methods targeted at reducing the effect of spurious correlations. Moreover, we show that the quality of learned feature representations is greatly affected by the design decisions beyond the training method, such as the model architecture and pre-training strategy. On the other hand, we find that strong regularization is not necessary for learning high quality feature representations. Finally, using insights from our analysis, we significantly improve upon the best results reported in the literature on the popular Waterbirds, CelebA hair color prediction and WILDS-FMOW problems, achieving 97 \%, 92 \% and 50 \% worst-group accuracies, respectively.},
  archiveprefix = {arXiv},
  eprint       = {2210.11369},
  primaryclass = {cs.LG},
  file         = {/home/ethan/org/pdfs/izmailov-2022-featur-learn.pdf}
}
@article{izmailov2021bayesian,
  title        = {What Are Bayesian Neural Network Posteriors Really Like?},
  author       = {Izmailov, Pavel and Vikram, Sharad and Hoffman, Matthew D and Wilson, Andrew Gordon},
  year         = 2021,
  journal      = {arXiv preprint arXiv:2104.14421}
}
@incollection{jaakkola1998improving,
  title        = {Improving the mean field approximation via the use of mixture distributions},
  author       = {Jaakkola, Tommi S and Jordan, Michael I},
  year         = 1998,
  booktitle    = {Learning in graphical models},
  publisher    = {Springer},
  pages        = {163--173}
}
@article{jia2014caffe,
  title        = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  author       = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  year         = 2014,
  journal      = {arXiv preprint arXiv:1408.5093}
}
@article{jiang2021layercam,
  title        = {Layercam: Exploring hierarchical class activation maps for localization},
  author       = {Jiang, Peng-Tao and Zhang, Chang-Bin and Hou, Qibin and Cheng, Ming-Ming and Wei, Yunchao},
  year         = 2021,
  journal      = {IEEE Transactions on Image Processing},
  publisher    = {IEEE},
  volume       = 30,
  pages        = {5875--5888}
}
@article{jordan1999introduction,
  title        = {An introduction to variational methods for graphical models},
  author       = {Jordan, Michael I and Ghahramani, Zoubin and Jaakkola, Tommi S and Saul, Lawrence K},
  year         = 1999,
  journal      = {Machine learning},
  publisher    = {Springer},
  volume       = 37,
  number       = 2,
  pages        = {183--233}
}
@inproceedings{kampffmeyer2016semantic,
  title        = {Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks},
  author       = {Kampffmeyer, Michael and Salberg, Arnt-Borre and Jenssen, Robert},
  year         = 2016,
  booktitle    = {Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
  pages        = {1--9}
}
@inproceedings{kendall2017bayes,
  title        = {Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding},
  author       = {Alex Kendall and Vijay Badrinarayanan and Roberto Cipolla},
  year         = 2017,
  booktitle    = {British Machine Vision Conference 2017, {BMVC} 2017, London, UK, September 4-7, 2017},
  publisher    = {{BMVA} Press},
  url          = {https://www.dropbox.com/s/jgozsaobbk98azy/0205.pdf?dl=1},
  timestamp    = {Fri, 23 Jul 2021 17:45:30 +0200},
  biburl       = {https://dblp.org/rec/conf/bmvc/KendallBC17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{kendall2017uncertainties,
  title        = {What uncertainties do we need in bayesian deep learning for computer vision?},
  author       = {Kendall, Alex and Gal, Yarin},
  year         = 2017,
  journal      = {Advances in neural information processing systems},
  volume       = 30
}
@article{khosravani-2022-activ-learn,
  title        = {Actively Learning Deep Neural Networks With Uncertainty Sampling Based on Sum-Product Networks},
  author       = {Khosravani, Mohamadsadegh and Zilles, Sandra},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.09798v1},
  abstract     = {Active learning is popular approach for reducing the amount of data in training deep neural network model. Its success hinges on the choice of an effective acquisition function, which ranks not yet labeled data points according to their expected informativeness. In uncertainty sampling, the uncertainty that the current model has about a point's class label is the main criterion for this type of ranking. This paper proposes a new approach to uncertainty sampling in training a Convolutional Neural Network (CNN). The main idea is to use feature representation extracted extracted by the CNN as data for training a Sum-Product Network (SPN). Since SPNs are typically used for estimating the distribution of a dataset, they are well suited to the task of estimating class probabilities that can be used directly by standard acquisition functions such as max entropy and variational ratio. Moreover, we enhance these acquisition functions by weights calculated with the help of the SPN model; these weights make the acquisition function more sensitive to the diversity of conceivable class labels for data points. The effectiveness of our method is demonstrated in an experimental study on the MNIST, Fashion-MNIST and CIFAR-10 datasets, where we compare it to the state-of-the-art methods MC Dropout and Bayesian Batch.},
  archiveprefix = {arXiv},
  eprint       = {2206.09798},
  primaryclass = {cs.LG}
}
@phdthesis{kim2015interactive,
  title        = {Interactive and interpretable machine learning models for human machine collaboration},
  author       = {Kim, Been},
  year         = 2015,
  school       = {Massachusetts Institute of Technology}
}
@article{kindermans2017learning,
  title        = {Learning how to explain neural networks: PatternNet and PatternAttribution},
  author       = {Kindermans, Pieter-Jan and Sch{\"u}tt, Kristof T and Alber, Maximilian and M{\"u}ller, Klaus-Robert and Erhan, Dumitru and Kim, Been and D{\"a}hne, Sven},
  year         = 2017,
  eprint       = {1705.05598},
  archiveprefix = {arXiv}
}
@article{kingma2013auto,
  title        = {Auto-encoding variational bayes},
  author       = {Kingma, Diederik P and Welling, Max},
  year         = 2013,
  journal      = {arXiv preprint arXiv:1312.6114},
  eprint       = {1312.6114},
  archiveprefix = {arXiv}
}
@inproceedings{kingma2014auto,
  title        = {Auto-Encoding Variational Bayes},
  author       = {Diederik P. Kingma and Max Welling},
  year         = 2014,
  booktitle    = {2nd International Conference on Learning Representations, {ICLR} 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings},
  url          = {http://arxiv.org/abs/1312.6114},
  editor       = {Yoshua Bengio and Yann LeCun},
  timestamp    = {Thu, 04 Apr 2019 13:20:07 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/KingmaW13.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{kingma2015adam,
  title        = {Adam: {A} Method for Stochastic Optimization},
  author       = {Diederik P. Kingma and Jimmy Ba},
  year         = 2015,
  booktitle    = {3rd International Conference on Learning Representations, {ICLR} 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings},
  url          = {http://arxiv.org/abs/1412.6980},
  editor       = {Yoshua Bengio and Yann LeCun},
  timestamp    = {Thu, 25 Jul 2019 14:25:37 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/KingmaB14.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{kingma2015variational,
  title        = {Variational dropout and the local reparameterization trick},
  author       = {Kingma, Diederik P and Salimans, Tim and Welling, Max},
  year         = 2015,
  booktitle    = {Advances in Neural Information Processing Systems},
  pages        = {2575--2583},
  url          = {https://proceedings.neurips.cc/paper/2015/hash/bc7316929fe1545bf0b98d114ee3ecb8-Abstract.html},
  bibsource    = {dblp computer science bibliography, https://dblp.org},
  biburl       = {https://dblp.org/rec/conf/nips/BlumHP15.bib},
  editor       = {Corinna Cortes and Neil D. Lawrence and Daniel D. Lee and Masashi Sugiyama and Roman Garnett},
  timestamp    = {Thu, 21 Jan 2021 00:00:00 +0100}
}
@article{klein1984time,
  title        = {A time-varying Poisson arrival process generator},
  author       = {Robert W. Klein and Stephen D. Roberts},
  year         = 1984,
  journal      = {Simulation},
  volume       = 43,
  pages        = {193--195}
}
@book{kochenderfer2022algorithms,
  title        = {Algorithms for decision making},
  author       = {Kochenderfer, Mykel J and Wheeler, Tim A and Wray, Kyle H},
  year         = 2022,
  publisher    = {MIT press}
}
@inproceedings{kohavi1996scaling,
  title        = {Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid.},
  author       = {Kohavi, Ron},
  year         = 1996,
  booktitle    = {Kdd},
  volume       = 96,
  pages        = {202--207},
  organization = {Citeseer}
}
@book{korotkov2020integrals,
  title        = {Integrals related to the error function},
  author       = {Korotkov, Nikolai E and Korotkov, Alexander N},
  year         = 2020,
  publisher    = {Chapman and Hall/CRC},
  file         = {~/org/pdfs/korotkov2020integrals.pdf}
}
@phdthesis{krak2016,
  title        = {Building Interpretable Models: From Bayesian Networks to Neural Networks},
  author       = {Krakovna,Viktoriya},
  year         = 2016,
  journal      = {ProQuest Dissertations and Theses},
  pages        = 85,
  isbn         = 9780355304961,
  keywords     = {Pure sciences; Applied sciences; Interpretability; Recurrent neural networks; Variable selection; Visualization; Statistics; Computer science; 0463:Statistics; 0984:Computer science},
  language     = {English}
}
@article{krishnan-2019-specif-weigh,
  title        = {Specifying Weight Priors in Bayesian Deep Neural Networks With Empirical Bayes},
  author       = {Krishnan, Ranganath and Subedar, Mahesh and Tickoo, Omesh},
  year         = 2019,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1906.05323v3},
  abstract     = {Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying meaningful weight priors is a challenging problem, particularly for scaling variational inference to deeper architectures involving high dimensional weight space. We propose MOdel Priors with Empirical Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian neural networks. We formulate a two-stage hierarchical modeling, first find the maximum likelihood estimates of weights with DNN, and then set the weight priors using empirical Bayes approach to infer the posterior with variational inference. We empirically evaluate the proposed approach on real-world tasks including image classification, video activity recognition and audio classification with varying complex neural network architectures. We also evaluate our proposed approach on diabetic retinopathy diagnosis task and benchmark with the state-of-the-art Bayesian deep learning techniques. We demonstrate MOPED method enables scalable variational inference and provides reliable uncertainty quantification.},
  archiveprefix = {arXiv},
  eprint       = {1906.05323},
  primaryclass = {cs.NE},
  file         = {/home/ethan/org/pdfs/krishnan-2019-specif-weigh.pdf}
}
@inproceedings{krishnan2020specifying,
  title        = {Specifying weight priors in bayesian deep neural networks with empirical bayes},
  author       = {Krishnan, Ranganath and Subedar, Mahesh and Tickoo, Omesh},
  year         = 2020,
  booktitle    = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume       = 34,
  number       = {04},
  pages        = {4477--4484},
  url          = {https://ojs.aaai.org/index.php/AAAI/article/view/5875}
}
@article{krizhevsky2009learning,
  title        = {Learning multiple layers of features from tiny images},
  author       = {Krizhevsky, Alex and Hinton, Geoffrey},
  year         = 2009,
  publisher    = {Citeseer}
}
@inproceedings{krizhevsky2012imagenet,
  title        = {Imagenet classification with deep convolutional neural networks},
  author       = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  year         = 2012,
  booktitle    = {Advances in neural information processing systems},
  pages        = {1097--1105}
}
@article{kucukelbir2016automatic,
  title        = {{Automatic Differentiation Variational Inference}},
  author       = {{Kucukelbir}, Alp and {Tran}, Dustin and {Ranganath}, Rajesh and {Gelman}, Andrew and {Blei}, David M.},
  year         = 2016,
  month        = {Mar},
  journal      = {arXiv e-prints},
  pages        = {arXiv:1603.00788},
  keywords     = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Computation},
  eid          = {arXiv:1603.00788},
  archiveprefix = {arXiv},
  eprint       = {1603.00788},
  primaryclass = {stat.ML},
  adsurl       = {https://ui.adsabs.harvard.edu/\#abs/2016arXiv160300788K},
  adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{kuleshov2018accurate,
  title        = {Accurate uncertainties for deep learning using calibrated regression},
  author       = {Kuleshov, Volodymyr and Fenner, Nathan and Ermon, Stefano},
  year         = 2018,
  journal      = {arXiv preprint arXiv:1807.00263}
}
@article{lampinen2001bayesian,
  title        = {Bayesian approach for neural networks--review and case studies},
  author       = {Lampinen, Jouko and Vehtari, Aki},
  year         = 2001,
  journal      = {Neural networks},
  publisher    = {Elsevier},
  volume       = 14,
  number       = 3,
  pages        = {257--274}
}
@online{lawrence2019deep,
  title        = {Deep Gaussian Processes},
  author       = {Neil Lawrence},
  year         = 2019,
  url          = {http://inverseprobability.com/talks/notes/deep-gaussian-processes.html},
  date         = {01/04/2019}
}
@article{lecun1989backpropagation,
  title        = {Backpropagation applied to handwritten zip code recognition},
  author       = {LeCun, Yann and Boser, Bernhard and Denker, John S and Henderson, Donnie and Howard, Richard E and Hubbard, Wayne and Jackel, Lawrence D},
  year         = 1989,
  journal      = {Neural computation},
  publisher    = {MIT Press},
  volume       = 1,
  number       = 4,
  pages        = {541--551}
}
@article{lecun1998gradient,
  title        = {Gradient-based learning applied to document recognition},
  author       = {LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  year         = 1998,
  journal      = {Proceedings of the IEEE},
  publisher    = {Ieee},
  volume       = 86,
  number       = 11,
  pages        = {2278--2324}
}
@article{lecun1998mnist,
  title        = {Gradient-based learning applied to document recognition},
  author       = {Y. Lecun and L. Bottou and Y. Bengio and P. Haffner},
  year         = 1998,
  month        = {Nov},
  journal      = {Proceedings of the IEEE},
  volume       = 86,
  number       = 11,
  pages        = {2278--2324},
  doi          = {10.1109/5.726791},
  issn         = {0018-9219},
  keywords     = {backpropagation;convolution;multilayer perceptrons;optical character recognition;2D shape variability;GTN;back-propagation;cheque reading;complex decision surface synthesis;convolutional neural network character recognizers;document recognition;document recognition systems;field extraction;gradient based learning technique;gradient-based learning;graph transformer networks;handwritten character recognition;handwritten digit recognition task;high-dimensional patterns;language modeling;multilayer neural networks;multimodule systems;performance measure minimization;segmentation recognition;Character recognition;Feature extraction;Hidden Markov models;Machine learning;Multi-layer neural network;Neural networks;Optical character recognition software;Optical computing;Pattern recognition;Principal component analysis}
}
@article{lecun2015deep,
  title        = {Deep learning},
  author       = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
  year         = 2015,
  journal      = {nature},
  publisher    = {Nature Publishing Group},
  volume       = 521,
  number       = 7553,
  pages        = 436
}
@article{lee-2022-asymp-proper,
  title        = {Asymptotic Properties for Bayesian Neural Network in Besov Space},
  author       = {Lee, Kyeongwon and Lee, Jaeyong},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.00241v1},
  abstract     = {Neural networks have shown great predictive power when dealing with various unstructured data such as images and natural languages. The Bayesian neural network captures the uncertainty of prediction by putting a prior distribution for the parameter of the model and computing the posterior distribution. In this paper, we show that the Bayesian neural network using spike-and-slab prior has consistency with nearly minimax convergence rate when the true regression function is in the Besov space. Even when the smoothness of the regression function is unknown the same posterior convergence rate holds and thus the spike and slab prior is adaptive to the smoothness of the regression function. We also consider the shrinkage prior and show that it has the same convergence rate. In other words, we propose a practical Bayesian neural network with guaranteed asymptotic properties.},
  archiveprefix = {arXiv},
  eprint       = {2206.00241},
  primaryclass = {stat.ML}
}
@inproceedings{lee2018deep,
  title        = {Deep Neural Networks as Gaussian Processes},
  author       = {Jaehoon Lee and Jascha Sohl-dickstein and Jeffrey Pennington and Roman Novak and Sam Schoenholz and Yasaman Bahri},
  year         = 2018,
  booktitle    = {International Conference on Learning Representations},
  url          = {https://openreview.net/forum?id=B1EA-M-0Z}
}
@article{letham2015,
  title        = {Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model},
  author       = {Letham, Benjamin and Rudin, Cynthia and McCormick, Tyler H. and Madigan, David},
  year         = 2015,
  month        = {09},
  journal      = {Ann. Appl. Stat.},
  publisher    = {The Institute of Mathematical Statistics},
  volume       = 9,
  number       = 3,
  pages        = {1350--1371},
  fjournal     = {The Annals of Applied Statistics}
}
@book{levin2017markov,
  title        = {Markov chains and mixing times},
  author       = {Levin, David A and Peres, Yuval},
  year         = 2017,
  publisher    = {American Mathematical Soc.},
  volume       = 107
}
@article{lewis1979simulation,
  title        = {Simulation of nonhomogeneous Poisson processes by thinning},
  author       = {Lewis, PA W and Shedler, Gerald S},
  year         = 1979,
  journal      = {Naval research logistics quarterly},
  publisher    = {Wiley Online Library},
  volume       = 26,
  number       = 3,
  pages        = {403--413}
}
@article{li2015preconditioned,
  title        = {Preconditioned stochastic gradient Langevin dynamics for deep neural networks},
  author       = {Li, Chunyuan and Chen, Changyou and Carlson, David and Carin, Lawrence},
  year         = 2015,
  journal      = {arXiv preprint arXiv:1512.07666}
}
@article{lipton2016mythos,
  title        = {The mythos of model interpretability},
  author       = {Lipton, Zachary C},
  year         = 2016,
  journal      = {CoRR},
  eprint       = {1606.03490},
  archiveprefix = {arXiv}
}
@article{lipton2018mythos,
  title        = {The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.},
  author       = {Lipton, Zachary C},
  year         = 2018,
  journal      = {Queue},
  publisher    = {ACM New York, NY, USA},
  volume       = 16,
  number       = 3,
  pages        = {31--57}
}
@article{liu19_under_mcmc_dynam_as_flows_wasser_space,
  title        = {Understanding Mcmc Dynamics As Flows on the Wasserstein Space},
  author       = {Liu, Chang and Zhuo, Jingwei and Zhu, Jun},
  year         = 2019,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1902.00282v3},
  abstract     = {It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel concepts, we propose a theoretical framework that recognizes a general MCMC dynamics as the fiber-gradient Hamiltonian flow on the Wasserstein space of a fiber-Riemannian Poisson manifold. The "conservation + convergence" structure of the flow gives a clear picture on the behavior of general MCMC dynamics. The framework also enables ParVI simulation of MCMC dynamics, which enriches the ParVI family with more efficient dynamics, and also adapts ParVI advantages to MCMCs. We develop two ParVI methods for a particular MCMC dynamics and demonstrate the benefits in experiments.},
  archiveprefix = {arXiv},
  eprint       = {1902.00282},
  primaryclass = {stat.ML}
}
@article{livnat2010sex,
  title        = {Sex, mixability, and modularity},
  author       = {Livnat, Adi and Papadimitriou, Christos and Pippenger, Nicholas and Feldman, Marcus W},
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  publisher    = {National Acad Sciences},
  volume       = 107,
  number       = 4,
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}
@article{lofstrom-2023-calib-explan,
  title        = {Calibrated Explanations: With Uncertainty Information and Counterfactuals},
  author       = {Lofstrom, Helena and Lofstrom, Tuwe and Johansson, Ulf and Sonstrod, Cecilia},
  year         = 2023,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2305.02305v1},
  abstract     = {Artificial Intelligence (AI) has become an integral part of decision support systems (DSSs) in various domains, but the lack of transparency in the predictive models used in AI-based DSSs can lead to misuse or disuse. Explainable Artificial Intelligence (XAI) aims to create AI systems that can explain their rationale to human users. Local explanations in XAI can provide information about the causes of individual predictions in terms of feature importance, but they suffer from drawbacks such as instability. To address these issues, we propose a new feature importance explanation method, Calibrated Explanations (CE), which is based on Venn-Abers and calibrates the underlying model while generating feature importance explanations. CE provides fast, reliable, stable, and robust explanations, along with uncertainty quantification of the probability estimates and feature importance weights. Furthermore, the method is model agnostic with easily understood conditional rules and can also generate counterfactual explanations with uncertainty quantification.},
  archiveprefix = {arXiv},
  eprint       = {2305.02305},
  primaryclass = {cs.AI},
  file         = {/home/ethan/org/pdfs/lofstrom-2023-calib-explan.pdf}
}
@inproceedings{long2015fully,
  title        = {Fully convolutional networks for semantic segmentation},
  author       = {Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
  year         = 2015,
  booktitle    = {Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages        = {3431--3440}
}
@article{loquercio_segu_2020,
  title        = {A General Framework for Uncertainty Estimation in Deep Learning},
  author       = {Loquercio, Antonio and Segu, Mattia and Scaramuzza, Davide},
  year         = 2020,
  journal      = {IEEE Robotics and Automation Letters},
  publisher    = {IEEE}
}
@inproceedings{louizos2016structured,
  title        = {Structured and efficient variational deep learning with matrix gaussian posteriors},
  author       = {Louizos, Christos and Welling, Max},
  year         = 2016,
  booktitle    = {International Conference on Machine Learning},
  pages        = {1708--1716}
}
@inproceedings{louizos2017multiplicative,
  title        = {Multiplicative Normalizing Flows for Variational Bayesian Neural Networks},
  author       = {Louizos, Christos and Welling, Max},
  year         = 2017,
  booktitle    = {Proceedings of the 34th International Conference on Machine Learning - Volume 70},
  location     = {Sydney, NSW, Australia},
  publisher    = {JMLR.org},
  series       = {Icml'17},
  pages        = {2218--2227},
  url          = {http://dl.acm.org/citation.cfm?id=3305890.3305910},
  numpages     = 10,
  acmid        = 3305910
}
@inproceedings{maas2013rectifier,
  title        = {Rectifier nonlinearities improve neural network acoustic models},
  author       = {Maas, Andrew L and Hannun, Awni Y and Ng, Andrew Y},
  year         = 2013,
  booktitle    = {Icml},
  volume       = 30,
  number       = 1,
  pages        = 3
}
@phdthesis{mackay1992bayesian,
  title        = {Bayesian methods for adaptive models},
  author       = {MacKay, David JC},
  year         = 1992,
  school       = {California Institute of Technology}
}
@article{mackay1992interp,
  title        = {Bayesian interpolation},
  author       = {MacKay, David JC},
  year         = 1992,
  journal      = {Neural computation},
  publisher    = {MIT Press},
  volume       = 4,
  number       = 3,
  pages        = {415--447}
}
@article{mackay1992practical,
  title        = {A practical Bayesian framework for backpropagation networks},
  author       = {MacKay, David JC},
  year         = 1992,
  journal      = {Neural computation},
  publisher    = {MIT Press},
  volume       = 4,
  number       = 3,
  pages        = {448--472}
}
@article{mackay1995probable,
  title        = {Probable networks and plausible predictions--a review of practical Bayesian methods for supervised neural networks},
  author       = {MacKay, David JC},
  year         = 1995,
  journal      = {Network: computation in neural systems},
  publisher    = {Taylor \& Francis},
  volume       = 6,
  number       = 3,
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}
@article{mackay1995review,
  title        = {Probable networks and plausible predictions -- a review of practical Bayesian methods for supervised neural networks},
  author       = {David J C Mackay},
  year         = 1995,
  journal      = {Network: Computation in Neural Systems},
  publisher    = {Taylor \& Francis},
  volume       = 6,
  number       = 3,
  pages        = {469--505}
}
@book{mackay2003information,
  title        = {Information theory, inference and learning algorithms},
  author       = {MacKay, David JC and Mac Kay, David JC},
  year         = 2003,
  publisher    = {Cambridge university press}
}
@article{maddison2016concrete,
  title        = {The concrete distribution: A continuous relaxation of discrete random variables},
  author       = {Maddison, Chris J and Mnih, Andriy and Teh, Yee Whye},
  year         = 2016,
  journal      = {arXiv preprint arXiv:1611.00712}
}
@inproceedings{maddox2019simple,
  title        = {A Simple Baseline for Bayesian Uncertainty in Deep Learning},
  author       = {Wesley J. Maddox and Pavel Izmailov and Timur Garipov and Dmitry P. Vetrov and Andrew Gordon Wilson},
  year         = 2019,
  booktitle    = {Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada},
  pages        = {13132--13143},
  url          = {https://proceedings.neurips.cc/paper/2019/hash/118921efba23fc329e6560b27861f0c2-Abstract.html},
  bibsource    = {dblp computer science bibliography, https://dblp.org},
  biburl       = {https://dblp.org/rec/conf/nips/MaddoxIGVW19.bib},
  editor       = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Roman Garnett},
  timestamp    = {Thu, 21 Jan 2021 00:00:00 +0100}
}
@article{madey2005agent,
  title        = {Agent-Based Scientific Simulation},
  author       = {G. Madey and X. Xiang and S. E. Cabaniss and Y. Huang},
  year         = 2005,
  month        = {jan},
  journal      = {Computing in Science \& Engineering},
  publisher    = {IEEE Computer Society},
  address      = {Los Alamitos, CA, USA},
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  pages        = {22--29},
  doi          = {10.1109/mcse.2005.7},
  issn         = {1521-9615},
  keywords     = {agents;simulation;scientific simulation;ecosystem}
}
@article{manchingal2022epistemic,
  title        = {Epistemic Deep Learning},
  author       = {Manchingal, Shireen Kudukkil and Cuzzolin, Fabio},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.07609v1},
  abstract     = {The belief function approach to uncertainty quantification as proposed in the Demspter-Shafer theory of evidence is established upon the general mathematical models for set-valued observations, called random sets. Set-valued predictions are the most natural representations of uncertainty in machine learning. In this paper, we introduce a concept called epistemic deep learning based on the random-set interpretation of belief functions to model epistemic learning in deep neural networks. We propose a novel random-set convolutional neural network for classification that produces scores for sets of classes by learning set-valued ground truth representations. We evaluate different formulations of entropy and distance measures for belief functions as viable loss functions for these random-set networks. We also discuss methods for evaluating the quality of epistemic predictions and the performance of epistemic random-set neural networks. We demonstrate through experiments that the epistemic approach produces better performance results when compared to traditional approaches of estimating uncertainty.},
  archiveprefix = {arXiv},
  eprint       = {2206.07609},
  primaryclass = {cs.LG}
}
@article{mandt2017stochastic,
  title        = {Stochastic Gradient Descent as Approximate Bayesian Inference},
  author       = {Mandt, Stephan and Hoffman, Matthew D and Blei, David M},
  year         = 2017,
  journal      = {Journal of Machine Learning Research},
  volume       = 18,
  pages        = {1--35}
}
@article{martens21_rapid_train_deep_neural_networ,
  title        = {Rapid Training of Deep Neural Networks Without Skip Connections Or Normalization Layers Using Deep Kernel Shaping},
  author       = {Martens, James and Ballard, Andy and Desjardins, Guillaume and Swirszcz, Grzegorz and Dalibard, Valentin and Sohl-Dickstein, Jascha and Schoenholz, Samuel S.},
  year         = 2021,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2110.01765v1},
  abstract     = {Using an extended and formalized version of the Q/C map analysis of Poole et al. (2016), along with Neural Tangent Kernel theory, we identify the main pathologies present in deep networks that prevent them from training fast and generalizing to unseen data, and show how these can be avoided by carefully controlling the "shape" of the network's initialization-time kernel function.  We then develop a method called Deep Kernel Shaping (DKS), which accomplishes this using a combination of precise parameter initialization, activation function transformations, and small architectural tweaks, all of which preserve the model class. In our experiments we show that DKS enables SGD training of residual networks without normalization layers on Imagenet and CIFAR-10 classification tasks at speeds comparable to standard ResNetV2 and Wide-ResNet models, with only a small decrease in generalization performance. And when using K-FAC as the optimizer, we achieve similar results for networks without skip connections. Our results apply for a large variety of activation functions, including those which traditionally perform very badly, such as the logistic sigmoid. In addition to DKS, we contribute a detailed analysis of skip connections, normalization layers, special activation functions like RELU and SELU, and various initialization schemes, explaining their effectiveness as alternative (and ultimately incomplete) ways of "shaping" the network's initialization-time kernel.},
  archiveprefix = {arXiv},
  eprint       = {2110.01765},
  primaryclass = {cs.LG}
}
@article{McCulloch1943,
  title        = {A logical calculus of the ideas immanent in nervous activity},
  author       = {McCulloch, Warren S. and Pitts, Walter},
  year         = 1943,
  month        = {Dec},
  day          = {01},
  journal      = {The bulletin of mathematical biophysics},
  volume       = 5,
  number       = 4,
  pages        = {115--133},
  doi          = {10.1007/bf02478259},
  issn         = {1522-9602},
  abstract     = {Because of the ``all-or-none'' character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed.}
}
@techreport{mckinsey2017,
  title        = {Smartening up with Artificial Intelligence (AI) - What's in it for Germany and its Industrial Sector?},
  year         = 2017,
  month        = 4,
  url          = {https://www.mckinsey.de/files/170419\_mckinsey\_ki\_final\_m.pdf},
  institution  = {McKinsey \& Company, Inc},
}
@article{melas-kyriazi21_do_you_even_need_atten,
  title        = {Do You Even Need Attention? a Stack of Feed-Forward Layers Does Surprisingly Well on Imagenet},
  author       = {Melas-Kyriazi, Luke},
  year         = 2021,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2105.02723v1},
  abstract     = {The strong performance of vision transformers on image classification and other vision tasks is often attributed to the design of their multi-head attention layers. However, the extent to which attention is responsible for this strong performance remains unclear. In this short report, we ask: is the attention layer even necessary? Specifically, we replace the attention layer in a vision transformer with a feed-forward layer applied over the patch dimension. The resulting architecture is simply a series of feed-forward layers applied over the patch and feature dimensions in an alternating fashion. In experiments on ImageNet, this architecture performs surprisingly well: a ViT/DeiT-base-sized model obtains 74.9\% top-1 accuracy, compared to 77.9\% and 79.9\% for ViT and DeiT respectively. These results indicate that aspects of vision transformers other than attention, such as the patch embedding, may be more responsible for their strong performance than previously thought. We hope these results prompt the community to spend more time trying to understand why our current models are as effective as they are.},
  archiveprefix = {arXiv},
  eprint       = {2105.02723},
  primaryclass = {cs.CV}
}
@article{milios-2018-diric-based,
  title        = {Dirichlet-Based Gaussian Processes for Large-Scale Calibrated Classification},
  author       = {Milios, Dimitrios and Camoriano, Raffaello and Michiardi, Pietro and Rosasco, Lorenzo and Filippone, Maurizio},
  year         = 2018,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1805.10915v1},
  abstract     = {In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled approach, but the corresponding computational burden is hardly sustainable in large-scale problems and devising efficient alternatives is a challenge. In this work, we investigate if and how Gaussian process regression directly applied to the classification labels can be used to tackle this question. While in this case training time is remarkably faster, predictions need be calibrated for classification and uncertainty estimation. To this aim, we propose a novel approach based on interpreting the labels as the output of a Dirichlet distribution. Extensive experimental results show that the proposed approach provides essentially the same accuracy and uncertainty quantification of Gaussian process classification while requiring only a fraction of computational resources.},
  archiveprefix = {arXiv},
  eprint       = {1805.10915},
  primaryclass = {cs.LG},
  file         = {/home/ethan/org/pdfs/milios-2018-diric-based.pdf}
}
@inproceedings{milioto2018real,
  title        = {Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs},
  author       = {Milioto, Andres and Lottes, Philipp and Stachniss, Cyrill},
  year         = 2018,
  booktitle    = {2018 IEEE international conference on robotics and automation (ICRA)},
  pages        = {2229--2235},
  organization = {IEEE}
}
@article{minderer2021revisiting,
  title        = {Revisiting the calibration of modern neural networks},
  author       = {Minderer, Matthias and Djolonga, Josip and Romijnders, Rob and Hubis, Frances and Zhai, Xiaohua and Houlsby, Neil and Tran, Dustin and Lucic, Mario},
  year         = 2021,
  journal      = {Advances in Neural Information Processing Systems},
  volume       = 34,
  pages        = {15682--15694}
}
@article{minka13_expec_propag_approx_bayes_infer,
  title        = {Expectation Propagation for Approximate Bayesian Inference},
  author       = {Minka, Thomas P.},
  year         = 2013,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1301.2294v1},
  abstract     = {This paper presents a new deterministic approximation technique in Bayesian networks. This method, "Expectation Propagation", unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks. All three algorithms try to recover an approximate distribution which is close in KL divergence to the true distribution. Loopy belief propagation, because it propagates exact belief states, is useful for a limited class of belief networks, such as those which are purely discrete. Expectation Propagation approximates the belief states by only retaining certain expectations, such as mean and variance, and iterates until these expectations are consistent throughout the network. This makes it applicable to hybrid networks with discrete and continuous nodes. Expectation Propagation also extends belief propagation in the opposite direction - it can propagate richer belief states that incorporate correlations between nodes. Experiments with Gaussian mixture models show Expectation Propagation to be convincingly better than methods with similar computational cost: Laplace's method, variational Bayes, and Monte Carlo. Expectation Propagation also provides an efficient algorithm for training Bayes point machine classifiers.},
  archiveprefix = {arXiv},
  eprint       = {1301.2294},
  primaryclass = {cs.AI}
}
@phdthesis{minka2001family,
  title        = {A family of algorithms for approximate Bayesian inference},
  author       = {Minka, Thomas Peter},
  year         = 2001,
  school       = {Massachusetts Institute of Technology}
}
@techreport{minka2005divergence,
  title        = {Divergence measures and message passing},
  author       = {Minka, Tom and others},
  year         = 2005,
  institution  = {Technical report, Microsoft Research}
}
@article{mohamed2012acoustic,
  title        = {Acoustic modeling using deep belief networks},
  author       = {Mohamed, Abdel and Dahl, George E and Hinton, Geoffrey},
  year         = 2012,
  journal      = {IEEE Transactions on Audio, Speech, and Language Processing},
  publisher    = {Ieee},
  volume       = 20,
  number       = 1,
  pages        = {14--22}
}
@article{mukhoti2018evaluating,
  title        = {Evaluating bayesian deep learning methods for semantic segmentation},
  author       = {Mukhoti, Jishnu and Gal, Yarin},
  year         = 2018,
  journal      = {arXiv preprint arXiv:1811.12709}
}
@article{mukhoti2021deep,
  title        = {Deep Deterministic Uncertainty for Semantic Segmentation},
  author       = {Mukhoti, Jishnu and van Amersfoort, Joost and Torr, Philip HS and Gal, Yarin},
  year         = 2021,
  journal      = {arXiv preprint arXiv:2111.00079}
}
@book{murphey2012machine,
  title        = {Machine learning, a probabilistic perspective},
  author       = {Murphey, kevin},
  year         = 2012,
  publisher    = {MIT Press},
  address      = {Cambridge, MA}
}
@article{mxnet2015,
  title        = {MXNet: {A} Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems},
  author       = {Tianqi Chen and Mu Li and Yutian Li and Min Lin and Naiyan Wang and Minjie Wang and Tianjun Xiao and Bing Xu and Chiyuan Zhang and Zheng Zhang},
  year         = 2015,
  journal      = {CoRR},
  volume       = {abs/1512.01274},
  url          = {http://arxiv.org/abs/1512.01274},
  archiveprefix = {arXiv},
  eprint       = {1512.01274},
  timestamp    = {Mon, 13 Aug 2018 16:48:04 +0200},
  biburl       = {https://dblp.org/rec/bib/journals/corr/ChenLLLWWXXZZ15},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{nagapetyan2017true,
  title        = {The true cost of stochastic gradient Langevin dynamics},
  author       = {Nagapetyan, Tigran and Duncan, Andrew B and Hasenclever, Leonard and Vollmer, Sebastian J and Szpruch, Lukasz and Zygalakis, Konstantinos},
  year         = 2017,
  journal      = {arXiv preprint arXiv:1706.02692}
}
@book{neal1996bayesian,
  title        = {Bayesian learning for neural networks},
  author       = {Neal, Radford M},
  year         = 1996,
  publisher    = {Springer Science \& Business Media},
  volume       = 118
}
@article{neal2004improving,
  title        = {Improving asymptotic variance of MCMC estimators: Non-reversible chains are better},
  author       = {Neal, Radford M},
  year         = 2004,
  journal      = {arXiv preprint math/0407281}
}
@article{neal2011mcmc,
  title        = {MCMC using Hamiltonian dynamics},
  author       = {Neal, Radford M and others},
  year         = 2011,
  journal      = {Handbook of markov chain monte carlo},
  volume       = 2,
  number       = 11,
  pages        = 2
}
@book{neal2012bayesian,
  title        = {Bayesian learning for neural networks},
  author       = {Neal, Radford M},
  year         = 2012,
  publisher    = {Springer Science \& Business Media},
  volume       = 118
}
@inproceedings{netzer2011reading,
  title        = {Reading digits in natural images with unsupervised feature learning},
  author       = {Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
  year         = 2011,
  booktitle    = {NIPS workshop on deep learning and unsupervised feature learning},
  volume       = 2011,
  number       = 2,
  pages        = 5
}
@inproceedings{nguyen2015deep,
  title        = {Deep neural networks are easily fooled: High confidence predictions for unrecognizable images},
  author       = {Nguyen, Anh and Yosinski, Jason and Clune, Jeff},
  year         = 2015,
  booktitle    = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages        = {427--436}
}
@inproceedings{novak2019bayesian,
  title        = {Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes},
  author       = {Roman Novak and Lechao Xiao and Yasaman Bahri and Jaehoon Lee and Greg Yang and Daniel A. Abolafia and Jeffrey Pennington and Jascha Sohl-dickstein},
  year         = 2019,
  booktitle    = {International Conference on Learning Representations},
  url          = {https://openreview.net/forum?id=B1g30j0qF7}
}
@article{oh2004gpu,
  title        = {GPU implementation of neural networks},
  author       = {Oh, Kyoung-Su and Jung, Keechul},
  year         = 2004,
  journal      = {Pattern Recognition},
  publisher    = {Elsevier},
  volume       = 37,
  number       = 6,
  pages        = {1311--1314}
}
@article{olah2017feature,
  title        = {Feature Visualization},
  author       = {Olah, Chris and Mordvintsev, Alexander and Schubert, Ludwig},
  year         = 2017,
  journal      = {Distill},
  doi          = {10.23915/distill.00007},
  note         = {https://distill.pub/2017/feature-visualization}
}
@article{olah2018the,
  title        = {The Building Blocks of Interpretability},
  author       = {Olah, Chris and Satyanarayan, Arvind and Johnson, Ian and Carter, Shan and Schubert, Ludwig and Ye, Katherine and Mordvintsev, Alexander},
  year         = 2018,
  journal      = {Distill},
  doi          = {10.23915/distill.00010}
}
@online{oord2017,
  title        = {WaveNet launches in the Google Assistant},
  author       = {A\"{a}ron van den Oord and Tom Walters and Trevor Strohman},
  url          = {https://deepmind.com/blog/wavenet-launches-google-assistant/},
  date         = {2017-10-04}
}
@article{opper1998bayesian,
  title        = {A Bayesian approach to on-line learning},
  author       = {Opper, Manfred and Winther, Ole},
  year         = 1998,
  journal      = {On-line learning in neural networks},
  publisher    = {Cambridge university press Cambridge},
  pages        = {363--378}
}
@article{opper2009TheVG,
  title        = {The Variational Gaussian Approximation Revisited},
  author       = {Manfred Opper and C{\'e}dric Archambeau},
  year         = 2009,
  journal      = {Neural computation},
  volume       = {21 3},
  pages        = {786--92}
}
@article{opper2009variational,
  title        = {The variational Gaussian approximation revisited},
  author       = {Opper, Manfred and Archambeau, C{\'e}dric},
  year         = 2009,
  journal      = {Neural computation},
  publisher    = {MIT Press},
  volume       = 21,
  number       = 3,
  pages        = {786--792}
}
@article{ortega-2022-deep-variat,
  title        = {Deep Variational Implicit Processes},
  author       = {Ortega, Luis A. and Santana, Sim{\'o}n Rodr{\'i}guez and Hern{\'a}ndez-Lobato, Daniel},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.06720v1},
  abstract     = {Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesian neural networks or neural samplers. IPs can be used as priors over functions, resulting in flexible models with well-calibrated prediction uncertainty estimates. Methods based on IPs usually carry out function-space approximate inference, which overcomes some of the difficulties of parameter-space approximate inference. Nevertheless, the approximations employed often limit the expressiveness of the final model, resulting, \emph{e.g.}, in a Gaussian predictive distribution, which can be restrictive. We propose here a multi-layer generalization of IPs called the Deep Variational Implicit process (DVIP). This generalization is similar to that of deep GPs over GPs, but it is more flexible due to the use of IPs as the prior distribution over the latent functions. We describe a scalable variational inference algorithm for training DVIP and show that it outperforms previous IP-based methods and also deep GPs. We support these claims via extensive regression and classification experiments. We also evaluate DVIP on large datasets with up to several million data instances to illustrate its good scalability and performance.},
  archiveprefix = {arXiv},
  eprint       = {2206.06720},
  primaryclass = {stat.ML},
  file         = {~/org/pdfs/ortega-2022-deep-variat.pdf}
}
@article{osband2016deep,
  title        = {Deep Exploration via Bootstrapped {DQN}},
  author       = {Ian Osband and Charles Blundell and Alexander Pritzel and Benjamin Van Roy},
  year         = 2016,
  journal      = {CoRR},
  volume       = {abs/1602.04621},
  url          = {http://arxiv.org/abs/1602.04621},
  archiveprefix = {arXiv},
  eprint       = {1602.04621},
  timestamp    = {Mon, 13 Aug 2018 16:46:32 +0200},
  biburl       = {https://dblp.org/rec/bib/journals/corr/OsbandBPR16},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{owen1980table,
  title        = {A table of normal integrals: A table},
  author       = {Owen, Donald Bruce},
  year         = 1980,
  journal      = {Communications in Statistics-Simulation and Computation},
  publisher    = {Taylor \& Francis},
  volume       = 9,
  number       = 4,
  pages        = {389--419}
}
@article{ozer-2022-how-to,
  title        = {How To Combine Variational Bayesian Networks in Federated Learning},
  author       = {Ozer, Atahan and Buldu, Kadir Burak and Akg{\"u}l, Abdullah and Unal, Gozde},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.10897v1},
  abstract     = {Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data. Even though deterministic models are capable of performing high prediction accuracy, their lack of calibration and capability to quantify uncertainty is problematic for safety-critical applications. Different from deterministic models, probabilistic models such as Bayesian neural networks are relatively well-calibrated and able to quantify uncertainty alongside their competitive prediction accuracy. Both of the approaches appear in the federated learning framework; however, the aggregation scheme of deterministic models cannot be directly applied to probabilistic models since weights correspond to distributions instead of point estimates. In this work, we study the effects of various aggregation schemes for variational Bayesian neural networks. With empirical results on three image classification datasets, we observe that the degree of spread for an aggregated distribution is a significant factor in the learning process. Hence, we present an investigation on the question of how to combine variational Bayesian networks in federated learning, while providing benchmarks for different aggregation settings.},
  archiveprefix = {arXiv},
  eprint       = {2206.10897},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/ozer-2022-how-to.pdf}
}
@article{paisley2012variational,
  title        = {Variational Bayesian inference with stochastic search},
  author       = {Paisley, John and Blei, David and Jordan, Michael},
  year         = 2012,
  journal      = {arXiv preprint arXiv:1206.6430}
}
@inproceedings{pakman2017stochastic,
  title        = {Stochastic bouncy particle sampler},
  author       = {Pakman, Ari and Gilboa, Dar and Carlson, David and Paninski, Liam},
  year         = 2017,
  booktitle    = {International Conference on Machine Learning},
  pages        = {2741--2750}
}
@article{papamarkou-2022-premis-approx,
  title        = {The Premise of Approximate Mcmc in Bayesian Deep Learning},
  author       = {Papamarkou, Theodore},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2208.11389v1},
  abstract     = {This paper identifies several characteristics of approximate MCMC in Bayesian deep learning. It proposes an approximate sampling algorithm for neural networks. By analogy to sampling data batches from big datasets, it is proposed to sample parameter subgroups from neural network parameter spaces of high dimensions. While the advantages of minibatch MCMC have been discussed in the literature, blocked Gibbs sampling has received less research attention in Bayesian deep learning.},
  archiveprefix = {arXiv},
  eprint       = {2208.11389},
  primaryclass = {stat.ML},
  file         = {~/org/pdfs/papamarkou-2022-premis-approx.pdf}
}
@inproceedings{park2018bam,
  title        = {BAM: Bottleneck Attention Module},
  author       = {Park, Jongchan and Woo, Sanghyun and Lee, Joon-Young and Kweon, In So},
  year         = 2018,
  booktitle    = {BMCV}
}
@inproceedings{parkhi12a,
  title        = {Cats and Dogs},
  author       = {Parkhi, O. M. and Vedaldi, A. and Zisserman, A. and Jawahar, C.~V.},
  year         = 2012,
  booktitle    = {IEEE Conference on Computer Vision and Pattern Recognition}
}
@article{paszke2016enet,
  title        = {Enet: A deep neural network architecture for real-time semantic segmentation},
  author       = {Paszke, Adam and Chaurasia, Abhishek and Kim, Sangpil and Culurciello, Eugenio},
  year         = 2016,
  journal      = {arXiv preprint arXiv:1606.02147}
}
@article{paszke2017automatic,
  title        = {Automatic differentiation in PyTorch},
  author       = {Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
  year         = 2017,
  booktitle    = {Proceedings of Neural Information Processing Systems}
}
@book{patel1996handbook,
  title        = {Handbook of the normal distribution},
  author       = {Patel, Jagdish K and Read, Campbell B},
  year         = 1996,
  publisher    = {CRC Press},
  volume       = 150
}
@incollection{patterson2013stochastic,
  title        = {Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex},
  author       = {Patterson, Sam and Teh, Yee Whye},
  year         = 2013,
  booktitle    = {Advances in Neural Information Processing Systems 26},
  publisher    = {Curran Associates, Inc.},
  pages        = {3102--3110},
  url          = {http://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf},
  editor       = {C. J. C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K. Q. Weinberger}
}
@article{pearl2009causal,
  title        = {Causal inference in statistics: An overview},
  author       = {Pearl, Judea and others},
  year         = 2009,
  journal      = {Statistics surveys},
  publisher    = {The author, under a Creative Commons Attribution License},
  volume       = 3,
  pages        = {96--146}
}
@article{peng2022pp,
  title        = {Pp-liteseg: A superior real-time semantic segmentation model},
  author       = {Peng, Juncai and Liu, Yi and Tang, Shiyu and Hao, Yuying and Chu, Lutao and Chen, Guowei and Wu, Zewu and Chen, Zeyu and Yu, Zhiliang and Du, Yuning and others},
  year         = 2022,
  journal      = {arXiv preprint arXiv:2204.02681}
}
@book{peters2017,
  title        = {Elements of Causal Inference},
  author       = {Jonas Peters and Dominik Janzing and Bernhard Scholkopf},
  year         = 2017,
  publisher    = {MIT Press},
  address      = {Cambridge},
  editor       = {Francis Bach}
}
@book{pml2022murphy,
  title        = {Probabilistic Machine Learning: An introduction},
  author       = {Kevin P. Murphy},
  year         = 2022,
  publisher    = {MIT Press},
  url          = {probml.ai}
}
@inproceedings{pmlr-v48-amodei16,
  title        = {Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin},
  author       = {Dario Amodei and Sundaram Ananthanarayanan and Rishita Anubhai and Jingliang Bai and Eric Battenberg and Carl Case and Jared Casper and Bryan Catanzaro and Qiang Cheng and Guoliang Chen and Jie Chen and Jingdong Chen and Zhijie Chen and Mike Chrzanowski and Adam Coates and Greg Diamos and Ke Ding and Niandong Du and Erich Elsen and Jesse Engel and Weiwei Fang and Linxi Fan and Christopher Fougner and Liang Gao and Caixia Gong and Awni Hannun and Tony Han and Lappi Johannes and Bing Jiang and Cai Ju and Billy Jun and Patrick LeGresley and Libby Lin and Junjie Liu and Yang Liu and Weigao Li and Xiangang Li and Dongpeng Ma and Sharan Narang and Andrew Ng and Sherjil Ozair and Yiping Peng and Ryan Prenger and Sheng Qian and Zongfeng Quan and Jonathan Raiman and Vinay Rao and Sanjeev Satheesh and David Seetapun and Shubho Sengupta and Kavya Srinet and Anuroop Sriram and Haiyuan Tang and Liliang Tang and Chong Wang and Jidong Wang and Kaifu Wang and Yi Wang and Zhijian Wang and Zhiqian Wang and Shuang Wu and Likai Wei and Bo Xiao and Wen Xie and Yan Xie and Dani Yogatama and Bin Yuan and Jun Zhan and Zhenyao Zhu},
  year         = 2016,
  month        = {20--22 Jun},
  booktitle    = {Proceedings of The 33rd International Conference on Machine Learning},
  publisher    = {Pmlr},
  address      = {New York, New York, USA},
  series       = {Proceedings of Machine Learning Research},
  volume       = 48,
  pages        = {173--182},
  editor       = {Maria Florina Balcan and Kilian Q. Weinberger},
  abstract     = {We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, enabling experiments that previously took weeks to now run in days. This allows us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.}
}
@article{popescu-2022-distr-gauss,
  title        = {Distributional Gaussian Processes Layers for Out-Of-Distribution Detection},
  author       = {Popescu, Sebastian G. and Sharp, David J. and Cole, James H. and Kamnitsas, Konstantinos and Glocker, Ben},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.13346v1},
  abstract     = {Machine learning models deployed on medical imaging tasks must be equipped with out-of-distribution detection capabilities in order to avoid erroneous predictions. It is unsure whether out-of-distribution detection models reliant on deep neural networks are suitable for detecting domain shifts in medical imaging. Gaussian Processes can reliably separate in-distribution data points from out-of-distribution data points via their mathematical construction. Hence, we propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has not been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions. To facilitate future work our code is publicly available.},
  archiveprefix = {arXiv},
  eprint       = {2206.13346},
  primaryclass = {cs.CV}
}
@article{popescu2022distributional,
  title        = {Distributional Gaussian Processes Layers for Out-Of-Distribution Detection},
  author       = {Popescu, Sebastian G. and Sharp, David J. and Cole, James H. and Kamnitsas, Konstantinos and Glocker, Ben},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.13346v1},
  abstract     = {Machine learning models deployed on medical imaging tasks must be equipped with out-of-distribution detection capabilities in order to avoid erroneous predictions. It is unsure whether out-of-distribution detection models reliant on deep neural networks are suitable for detecting domain shifts in medical imaging. Gaussian Processes can reliably separate in-distribution data points from out-of-distribution data points via their mathematical construction. Hence, we propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has not been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions. To facilitate future work our code is publicly available.},
  archiveprefix = {arXiv},
  eprint       = {2206.13346},
  primaryclass = {cs.CV}
}
@article{proscura-2022-effec-train,
  title        = {Effective Training-Time Stacking for Ensembling of Deep Neural Networks},
  author       = {Proscura, Polina and Zaytsev, Alexey},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.13491v1},
  abstract     = {Ensembling is a popular and effective method for improving machine learning (ML) models. It proves its value not only in classical ML but also for deep learning. Ensembles enhance the quality and trustworthiness of ML solutions, and allow uncertainty estimation. However, they come at a price: training ensembles of deep learning models eat a huge amount of computational resources. A snapshot ensembling collects models in the ensemble along a single training path. As it runs training only one time, the computational time is similar to the training of one model. However, the quality of models along the training path is different: typically, later models are better if no overfitting occurs. So, the models are of varying utility. Our method improves snapshot ensembling by selecting and weighting ensemble members along the training path. It relies on training-time likelihoods without looking at validation sample errors that standard stacking methods do. Experimental evidence for Fashion MNIST, CIFAR-10, and CIFAR-100 datasets demonstrates the superior quality of the proposed weighted ensembles c.t. vanilla ensembling of deep learning models.},
  archiveprefix = {arXiv},
  eprint       = {2206.13491},
  primaryclass = {cs.LG}
}
@article{putter-2022-how-to,
  title        = {How To Train Accurate Bnns for Embedded Systems?},
  author       = {Putter, Floran de and Corporaal, Henk},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.12322v1},
  abstract     = {A key enabler of deploying convolutional neural networks on resource-constrained embedded systems is the binary neural network (BNN). BNNs save on memory and simplify computation by binarizing both features and weights. Unfortunately, binarization is inevitably accompanied by a severe decrease in accuracy. To reduce the accuracy gap between binary and full-precision networks, many repair methods have been proposed in the recent past, which we have classified and put into a single overview in this chapter. The repair methods are divided into two main branches, training techniques and network topology changes, which can further be split into smaller categories. The latter category introduces additional cost (energy consumption or additional area) for an embedded system, while the former does not. From our overview, we observe that progress has been made in reducing the accuracy gap, but BNN papers are not aligned on what repair methods should be used to get highly accurate BNNs. Therefore, this chapter contains an empirical review that evaluates the benefits of many repair methods in isolation over the ResNet-20\&CIFAR10 and ResNet-18\&CIFAR100 benchmarks. We found three repair categories most beneficial: feature binarizer, feature normalization, and double residual. Based on this review we discuss future directions and research opportunities. We sketch the benefit and costs associated with BNNs on embedded systems because it remains to be seen whether BNNs will be able to close the accuracy gap while staying highly energy-efficient on resource-constrained embedded systems.},
  archiveprefix = {arXiv},
  eprint       = {2206.12322},
  primaryclass = {cs.LG}
}
@inproceedings{qu2021bayesian,
  title        = {Bayesian deep basis fitting for depth completion with uncertainty},
  author       = {Qu, Chao and Liu, Wenxin and Taylor, Camillo J},
  year         = 2021,
  booktitle    = {Proceedings of the IEEE/CVF international conference on computer vision},
  pages        = {16147--16157}
}
@article{raj-2022-look-beyon-label,
  title        = {Look Beyond Labels: Incorporating Functional Summary Information in Bayesian Neural Networks},
  author       = {Raj, Vishnu and Cui, Tianyu and Heinonen, Markus and Marttinen, Pekka},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2207.01234v1},
  abstract     = {Bayesian deep learning offers a principled approach to train neural networks that accounts for both aleatoric and epistemic uncertainty. In variational inference, priors are often specified over the weight parameters, but they do not capture the true prior knowledge in large and complex neural network architectures. We present a simple approach to incorporate summary information about the predicted probability (such as sigmoid or softmax score) outputs in Bayesian neural networks (BNNs). The available summary information is incorporated as augmented data and modeled with a Dirichlet process, and we derive the corresponding \emph{Summary Evidence Lower BOund}. We show how the method can inform the model about task difficulty or class imbalance. Extensive empirical experiments show that, with negligible computational overhead, the proposed method yields a BNN with a better calibration of uncertainty.},
  archiveprefix = {arXiv},
  eprint       = {2207.01234},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/raj-2022-look-beyon-label.pdf}
}
@article{ramanan2018,
  title        = {Bounds on Lifting Continuous-State Markov Chains to Speed Up Mixing},
  author       = {Ramanan, Kavita and Smith, Aaron},
  year         = 2018,
  month        = {Sep},
  day          = {01},
  journal      = {Journal of Theoretical Probability},
  volume       = 31,
  number       = 3,
  pages        = {1647--1678},
  doi          = {10.1007/s10959-017-0745-5},
  issn         = {1572-9230},
  url          = {https://doi.org/10.1007/s10959-017-0745-5},
  abstract     = {It is often possible to speed up the mixing of a Markov chain {\$}{\$}{\backslash}{\{} X{\_}{\{}t{\}} {\backslash}{\}}{\_}{\{}t {\backslash}in {\backslash}mathbb {\{}N{\}}{\}}{\$}{\$}{\{}Xt{\}}t\in{}Non a state space {\$}{\$}{\backslash}Omega {\$}{\$}\$\Omega\$by lifting, that is, running a more efficient Markov chain {\$}{\$}{\backslash}{\{} {\backslash}widehat{\{}X{\}}{\_}{\{}t{\}} {\backslash}{\}}{\_}{\{}t {\backslash}in {\backslash}mathbb {\{}N{\}}{\}}{\$}{\$}{\{}X^t{\}}t\in{}Non a larger state space {\$}{\$}{\backslash}hat{\{}{\backslash}Omega {\}} {\backslash}supset {\backslash}Omega {\$}{\$}\$\Omega\$^\supset{}\$\Omega\$that projects to {\$}{\$}{\backslash}{\{} X{\_}{\{}t{\}} {\backslash}{\}}{\_}{\{}t {\backslash}in {\backslash}mathbb {\{}N{\}}{\}}{\$}{\$}{\{}Xt{\}}t\in{}Nin a certain sense. Chen et al. (Proceedings of the 31st annual ACM symposium on theory of computing. ACM, 1999) prove that for Markov chains on finite state spaces, the mixing time of any lift of a Markov chain is at least the square root of the mixing time of the original chain, up to a factor that depends on the stationary measure of {\$}{\$}{\backslash}{\{}X{\_}t{\backslash}{\}}{\_}{\{}t {\backslash}in {\backslash}mathbb {\{}N{\}}{\}}{\$}{\$}{\{}Xt{\}}t\in{}N. Unfortunately, this extra factor makes the bound in Chen et al. (1999) very loose for Markov chains on large state spaces and useless for Markov chains on continuous state spaces. In this paper, we develop an extension of the evolving set method that allows us to refine this extra factor and find bounds for Markov chains on continuous state spaces that are analogous to the bounds in Chen et al. (1999). These bounds also allow us to improve on the bounds in Chen et al. (1999) for some chains on finite state spaces.}
}
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}
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  abstract     = {To answer the questions of how information about the physical world is sensed, in what form is information remembered, and how does information retained in memory influence recognition and behavior, a theory is developed for a hypothetical nervous system called a perceptron. The theory serves as a bridge between biophysics and psychology. It is possible to predict learning curves from neurological variables and vice versa. The quantitative statistical approach is fruitful in the understanding of the organization of cognitive systems. 18 references. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
  keywords     = {probabilistic model, information storage, brain, Brain, Humans, Information Storage and Retrieval, Models, Statistical, Neural Networks (Computer), Perception, Brain, Cognition, Memory, Nervous System}
}
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@article{sharma-2022-do-bayes,
  title        = {Do Bayesian Neural Networks Need To Be Fully Stochastic?},
  author       = {Sharma, Mrinank and Farquhar, Sebastian and Nalisnick, Eric and Rainforth, Tom},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2211.06291v1},
  abstract     = {We investigate the efficacy of treating all the parameters in a Bayesian neural network stochastically and find compelling theoretical and empirical evidence that this standard construction may be unnecessary. To this end, we prove that expressive predictive distributions require only small amounts of stochasticity. In particular, partially stochastic networks with only $n$ stochastic biases are universal probabilistic predictors for \$n\$-dimensional predictive problems. In empirical investigations, we find no systematic benefit of full stochasticity across four different inference modalities and eight datasets; partially stochastic networks can match and sometimes even outperform fully stochastic networks, despite their reduced memory costs.},
  archiveprefix = {arXiv},
  eprint       = {2211.06291},
  primaryclass = {cs.LG},
  file         = {/home/ethan/org/pdfs/sharma-2022-do-bayes.pdf}
}
@inproceedings{shrivastava2016training,
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@article{silver2017mastering,
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@inproceedings{szegedy2015going,
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@inproceedings{szegedy2016rethinking,
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  url          = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpa.21423},
  eprint       = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpa.21423},
  abstract     = {Abstract A new methodology for density estimation is proposed. The methodology, which builds on the one developed by Tabak and Vanden-Eijnden, normalizes the data points through the composition of simple maps. The parameters of each map are determined through the maximization of a local quadratic approximation to the log-likelihood. Various candidates for the elementary maps of each step are proposed; criteria for choosing one includes robustness, computational simplicity, and good behavior in high-dimensional settings. A good choice is that of localized radial expansions, which depend on a single parameter: all the complexity of arbitrary, possibly convoluted probability densities can be built through the composition of such simple maps. \textcopyright{} 2012 Wiley Periodicals, Inc.}
}
@article{teh2016consistency,
  title        = {Consistency and fluctuations for stochastic gradient Langevin dynamics},
  author       = {Teh, Yee Whye and Thiery, Alexandre H and Vollmer, Sebastian J},
  year         = 2016,
  journal      = {Journal of Machine Learning Research},
  publisher    = {Journal of Machine Learning Research},
  volume       = 17
}
@article{tensorflow2016,
  title        = {TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems},
  author       = {Mart{\'{\i}}n Abadi and Ashish Agarwal and Paul Barham and Eugene Brevdo and Zhifeng Chen and Craig Citro and Gregory S. Corrado and Andy Davis and Jeffrey Dean and Matthieu Devin and Sanjay Ghemawat and Ian J. Goodfellow and Andrew Harp and Geoffrey Irving and Michael Isard and Yangqing Jia and Rafal J{\'{o}}zefowicz and Lukasz Kaiser and Manjunath Kudlur and Josh Levenberg and Dan Man{\'{e}} and Rajat Monga and Sherry Moore and Derek Gordon Murray and Chris Olah and Mike Schuster and Jonathon Shlens and Benoit Steiner and Ilya Sutskever and Kunal Talwar and Paul A. Tucker and Vincent Vanhoucke and Vijay Vasudevan and Fernanda B. Vi{\'{e}}gas and Oriol Vinyals and Pete Warden and Martin Wattenberg and Martin Wicke and Yuan Yu and Xiaoqiang Zheng},
  year         = 2016,
  journal      = {CoRR},
  volume       = {abs/1603.04467},
  url          = {http://arxiv.org/abs/1603.04467},
  archiveprefix = {arXiv},
  eprint       = {1603.04467},
  timestamp    = {Mon, 13 Aug 2018 16:47:09 +0200},
  biburl       = {https://dblp.org/rec/bib/journals/corr/AbadiABBCCCDDDG16},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@online{tesla2016,
  title        = {A Tragic Loss},
  author       = {Tesla{\ }Team},
  url          = {https://www.tesla.com/en\_GB/blog/tragic-loss},
  date         = {2016-06-30}
}
@book{theo2006,
  title        = {Pattern recognition},
  author       = {S. Theodoridis and K. Koutroumbas},
  year         = 2006,
  publisher    = {Elsevier},
  address      = {Amsterdam},
  isbn         = {0123695317;9780123695314;},
  edition      = 3,
  keywords     = {Pattern recognition systems; Applications of Computing; Data Modeling \& Design; COMPUTERS},
  language     = {English}
}
@inproceedings{tipping2000relevance,
  title        = {The relevance vector machine},
  author       = {Tipping, Michael E},
  year         = 2000,
  booktitle    = {Advances in neural information processing systems},
  pages        = {652--658}
}
@article{tipping2001,
  title        = {Sparse Bayesian learning and the relevance vector machine},
  author       = {Tipping, Michael E},
  year         = 2001,
  journal      = {Journal of machine learning research},
  volume       = 1,
  pages        = {211--244}
}
@inproceedings{tishby1989,
  title        = {Consistent inference of probabilities in layered networks: predictions and generalizations},
  author       = {N. Tishby and E. Levin and S. A. Solla},
  year         = 1989,
  booktitle    = {International 1989 Joint Conference on Neural Networks},
  pages        = {403--409 vol.2},
  doi          = {10.1109/ijcnn.1989.118274},
  keywords     = {learning systems;neural nets;probability;Gibbs distribution;consistency condition;error minimization;general input-output relation;layered networks;learning;learning curves;neural network;optimal architecture;probabilities;statistical formalism;statistical framework;training set;training size;Learning systems;Neural networks;Probability}
}
@inproceedings{titsias2009variational,
  title        = {Variational Learning of Inducing Variables in Sparse Gaussian Processes},
  author       = {Michalis Titsias},
  year         = 2009,
  month        = {16--18 Apr},
  booktitle    = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics},
  publisher    = {Pmlr},
  address      = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA},
  series       = {Proceedings of Machine Learning Research},
  volume       = 5,
  pages        = {567--574},
  url          = {http://proceedings.mlr.press/v5/titsias09a.html},
  editor       = {David van Dyk and Max Welling},
  pdf          = {http://proceedings.mlr.press/v5/titsias09a/titsias09a.pdf},
  abstract     = {Sparse Gaussian process methods that use inducing variables require the selection of the inducing inputs and the kernel hyperparameters. We introduce a variational formulation for sparse approximations that jointly infers the inducing inputs and the kernel hyperparameters by maximizing a lower bound of the true log marginal likelihood. The key property of this formulation is that the inducing inputs  are defined to be variational parameters  which are selected by minimizing  the Kullback-Leibler divergence between  the variational distribution and the exact posterior distribution over the latent function values. We apply this technique to regression and we compare it with other approaches in the literature.}
}
@article{tolstikhin21_mlp_mixer,
  title        = {Mlp-Mixer: an All-Mlp Architecture for Vision},
  author       = {Tolstikhin, Ilya and Houlsby, Neil and Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Unterthiner, Thomas and Yung, Jessica and Steiner, Andreas and Keysers, Daniel and Uszkoreit, Jakob and Lucic, Mario and Dosovitskiy, Alexey},
  year         = 2021,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2105.01601v4},
  abstract     = {Convolutional Neural Networks (CNNs) are the go-to model for computer vision.  Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this paper we show that while convolutions and attention are both sufficient for good performance, neither of them are necessary. We present MLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs).  MLP-Mixer contains two types of layers: one with MLPs applied independently to image patches (i.e. "mixing" the per-location features), and one with MLPs applied across patches (i.e. "mixing" spatial information). When trained on large datasets, or with modern regularization schemes, MLP-Mixer attains competitive scores on image classification benchmarks, with pre-training and inference cost comparable to state-of-the-art models. We hope that these results spark further research beyond the realms of well established CNNs and Transformers.},
  archiveprefix = {arXiv},
  eprint       = {2105.01601v4},
  primaryclass = {cs.CV}
}
@article{touvron21_resml,
  title        = {Resmlp: Feedforward Networks for Image Classification With Data-Efficient Training},
  author       = {Touvron, Hugo and Bojanowski, Piotr and Caron, Mathilde and Cord, Matthieu and El-Nouby, Alaaeldin and Grave, Edouard and Izacard, Gautier and Joulin, Armand and Synnaeve, Gabriel and Verbeek, Jakob and J{\'e}gou, Herv{\'e}},
  year         = 2021,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2105.03404v2},
  abstract     = {We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which image patches interact, independently and identically across channels, and (ii) a two-layer feed-forward network in which channels interact independently per patch. When trained with a modern training strategy using heavy data-augmentation and optionally distillation, it attains surprisingly good accuracy/complexity trade-offs on ImageNet. We also train ResMLP models in a self-supervised setup, to further remove priors from employing a labelled dataset. Finally, by adapting our model to machine translation we achieve surprisingly good results. We share pre-trained models and our code based on the Timm library.},
  archiveprefix = {arXiv},
  eprint       = {2105.03404},
  primaryclass = {cs.CV}
}
@article{tran-2022-plex,
  title        = {Plex: Towards Reliability Using Pretrained Large Model Extensions},
  author       = {Tran, Dustin and Liu, Jeremiah and Dusenberry, Michael W. and Phan, Du and Collier, Mark and Ren, Jie and Han, Kehang and Wang, Zi and Mariet, Zelda and Hu, Huiyi and Band, Neil and Rudner, Tim G. J. and Singhal, Karan and Nado, Zachary and Amersfoort, Joost van and Kirsch, Andreas and Jenatton, Rodolphe and Thain, Nithum and Yuan, Honglin and Buchanan, Kelly and Murphy, Kevin and Sculley, D. and Gal, Yarin and Ghahramani, Zoubin and Snoek, Jasper and Lakshminarayanan, Balaji},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2207.07411v1},
  abstract     = {A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical to the field. In this paper, we explore the reliability of models, where we define a reliable model as one that not only achieves strong predictive performance but also performs well consistently over many decision-making tasks involving uncertainty (e.g., selective prediction, open set recognition), robust generalization (e.g., accuracy and proper scoring rules such as log-likelihood on in- and out-of-distribution datasets), and adaptation (e.g., active learning, few-shot uncertainty). We devise 10 types of tasks over 40 datasets in order to evaluate different aspects of reliability on both vision and language domains. To improve reliability, we developed ViT-Plex and T5-Plex, pretrained large model extensions for vision and language modalities, respectively. Plex greatly improves the state-of-the-art across reliability tasks, and simplifies the traditional protocol as it improves the out-of-the-box performance and does not require designing scores or tuning the model for each task. We demonstrate scaling effects over model sizes up to 1B parameters and pretraining dataset sizes up to 4B examples. We also demonstrate Plex's capabilities on challenging tasks including zero-shot open set recognition, active learning, and uncertainty in conversational language understanding.},
  archiveprefix = {arXiv},
  eprint       = {2207.07411},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/tran-2022-plex.pdf}
}
@article{trinh-2022-tackl-covar,
  title        = {Tackling Covariate Shift With Node-Based Bayesian Neural Networks},
  author       = {Trinh, Trung and Heinonen, Markus and Acerbi, Luigi and Kaski, Samuel},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.02435v2},
  abstract     = {Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weight-based BNNs often struggle with high computational complexity of large-scale architectures and datasets. Node-based BNNs have recently been introduced as scalable alternatives, which induce epistemic uncertainty by multiplying each hidden node with latent random variables, while learning a point-estimate of the weights. In this paper, we interpret these latent noise variables as implicit representations of simple and domain-agnostic data perturbations during training, producing BNNs that perform well under covariate shift due to input corruptions. We observe that the diversity of the implicit corruptions depends on the entropy of the latent variables, and propose a straightforward approach to increase the entropy of these variables during training. We evaluate the method on out-of-distribution image classification benchmarks, and show improved uncertainty estimation of node-based BNNs under covariate shift due to input perturbations. As a side effect, the method also provides robustness against noisy training labels.},
  archiveprefix = {arXiv},
  eprint       = {2206.02435},
  primaryclass = {stat.ML},
  file         = {~/org/pdfs/trinh-2022-tackl-covar.pdf}
}
@incollection{turner2011two,
  title        = {Two problems with variational expectation maximisation for time-series models},
  author       = {Turner, R. E. and Sahani, M.},
  year         = 2011,
  booktitle    = {Bayesian Time Series Models},
  publisher    = {Cambridge University Press},
  editor       = {Barber, D. and Cemgil, A. T. and Chiappa, S.}
}
@techreport{van2017,
  title        = {Artificial intelligence in wealth and asset management},
  author       = {Edgar Van Tuyll Van Serooskerken},
  year         = 2017,
  month        = 1,
  url          = {https://perspectives.pictet.com/wp-content/uploads/2016/12/Edgar-van-Tuyll-van-Serooskerken-Pictet-Report-winter-2016-2.pdf},
  institution  = {Pictet on Robot Advisors},
}
@inproceedings{van2017convolutional,
  title        = {Convolutional gaussian processes},
  author       = {Van der Wilk, Mark and Rasmussen, Carl Edward and Hensman, James},
  year         = 2017,
  booktitle    = {Advances in Neural Information Processing Systems},
  pages        = {2849--2858}
}
@inproceedings{van2020uncertainty,
  title        = {Uncertainty estimation using a single deep deterministic neural network},
  author       = {Van Amersfoort, Joost and Smith, Lewis and Teh, Yee Whye and Gal, Yarin},
  year         = 2020,
  booktitle    = {International conference on machine learning},
  pages        = {9690--9700},
  organization = {PMLR}
}
@article{vanetti2017piecewise,
  title        = {Piecewise-deterministic markov chain monte carlo},
  author       = {Vanetti, Paul and Bouchard-C{\^o}t{\'e}, Alexandre and Deligiannidis, George and Doucet, Arnaud},
  year         = 2017,
  journal      = {arXiv preprint arXiv:1707.05296}
}
@article{vapnik1999overview,
  title        = {An overview of statistical learning theory},
  author       = {Vapnik, Vladimir Naumovich},
  year         = 1999,
  journal      = {IEEE transactions on neural networks},
  publisher    = {Ieee},
  volume       = 10,
  number       = 5,
  pages        = {988--999}
}
@book{vapnik2000,
  title        = {The Nature of Statistical Learning Theory},
  author       = {Vapnik, Vladimir, N.},
  year         = 2000,
  publisher    = {Springer},
  address      = {Ny},
  edition      = 2
}
@article{vaswani17_atten_is_all_you_need,
  title        = {Attention Is All You Need},
  author       = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
  year         = 2017,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1706.03762v5},
  abstract     = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
  archiveprefix = {arXiv},
  eprint       = {1706.03762},
  primaryclass = {cs.CL},
  file         = {~/org/pdfs/vaswani17\_atten\_is\_all\_you\_need.pdf}
}
@inproceedings{vladimirova2019understanding,
  title        = {Understanding priors in bayesian neural networks at the unit level},
  author       = {Vladimirova, Mariia and Verbeek, Jakob and Mesejo, Pablo and Arbel, Julyan},
  year         = 2019,
  booktitle    = {International Conference on Machine Learning},
  pages        = {6458--6467},
  organization = {PMLR}
}
@article{vollmer2015non,
  title        = {(Non-) asymptotic properties of stochastic gradient Langevin dynamics},
  author       = {Vollmer, Sebastian J and Zygalakis, Konstantinos C and others},
  year         = 2015,
  journal      = {arXiv preprint arXiv:1501.00438}
}
@article{vu2018,
  title        = {A Shared Vision for Machine Learning in Neuroscience},
  author       = {Vu,MAT and Adali,T. and Ba,D. and Buzsaki,G. and Carlson,D. and Heller,K. and Liston,C. and Rudin,C. and Sohal,VS and Widge,AS and Mayberg,HS and Sapiro,G. and Dzirasa,K.},
  year         = 2018,
  journal      = {Journal Of Neuroscience},
  volume       = 38,
  number       = 7,
  pages        = {1601--1607},
  abstract     = {With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.},
  keywords     = {explainable artificial intelligence; DEPRESSION; STIMULATION; BRAIN IMAGES; machine learning; NEUROSCIENCES; reinforcement learning; INDEPENDENT COMPONENT ANALYSIS; MEMORY; BIG DATA; CORTEX; REINFORCEMENT; OPTIMIZATION; REGISTRATION},
  language     = {English}
}
@article{vucelja2016lifting,
  title        = {Lifting--a nonreversible Markov chain Monte Carlo algorithm},
  author       = {Vucelja, Marija},
  year         = 2016,
  journal      = {American Journal of Physics},
  publisher    = {Aapt},
  volume       = 84,
  number       = 12,
  pages        = {958--968}
}
@book{wainwright2008graphical,
  title        = {Graphical models, exponential families, and variational inference},
  author       = {Wainwright, Martin J and Jordan, Michael Irwin},
  year         = 2008,
  journal      = {Foundations and Trends{\textregistered} in Machine Learning},
  publisher    = {Now Publishers Inc},
  volume       = 1,
  number       = {1--2},
  pages        = {1--305}
}
@online{wakefield2016,
  title        = {Microsoft chatbot is taught to swear on Twitter},
  author       = {Jane Wakefield},
  url          = {www.bbc.com/news/technology-35890188},
  date         = {2016-05-04},
  editor       = {{BBC News}}
}
@article{wang-2016-natur-param-networ,
  title        = {Natural-Parameter Networks: a Class of Probabilistic Neural Networks},
  author       = {Wang, Hao and Shi, Xingjian and Yeung, Dit-Yan},
  year         = 2016,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1611.00448v1},
  abstract     = {Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.},
  archiveprefix = {arXiv},
  eprint       = {1611.00448},
  primaryclass = {cs.LG},
  file         = {/home/ethan/org/pdfs/wang-2016-natur-param-networ.pdf}
}
@article{wang-2022-out-distr,
  title        = {Out of Distribution Robustness With Pre-Trained Bayesian Neural Networks},
  author       = {Wang, Xi and Aitchison, Laurence},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.12361v1},
  abstract     = {We develop ShiftMatch, a new training-data-dependent likelihood for out of distribution (OOD) robustness in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-dependent "EmpCov" priors from Izmailov et al. (2021a) and efficiently matches test-time spatial correlations to those at training time. Critically, ShiftMatch is designed to leave neural network training unchanged, allowing it to use publically available samples from pretrained BNNs. Using pre-trained HMC samples, ShiftMatch gives strong performance improvements on CIFAR-10-C, outperforms EmpCov priors, and is perhaps the first Bayesian method capable of convincingly outperforming plain deep ensembles. ShiftMatch can be integrated with non-Bayesian methods like deep ensembles, where it offers smaller, but still considerable, performance improvements. Overall, Bayesian ShiftMatch gave slightly better accuracy than ensembles with ShiftMatch, though they both had very similar log-likelihoods.},
  archiveprefix = {arXiv},
  eprint       = {2206.12361},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/wang-2022-out-distr.pdf}
}
@inproceedings{wang2005inadequacy,
  title        = {Inadequacy of interval estimates corresponding to variational Bayesian approximations.},
  author       = {Wang, Bo and Titterington, DM},
  year         = 2005,
  booktitle    = {Aistats},
  organization = {Barbados}
}
@inproceedings{wang2013fast,
  title        = {Fast dropout training},
  author       = {Wang, Sida and Manning, Christopher},
  year         = 2013,
  booktitle    = {international conference on machine learning},
  pages        = {118--126}
}
@inproceedings{wang2016,
  title        = {Bayesian Rule Sets for Interpretable Classification},
  author       = {T. Wang and C. Rudin and F. Velez-Doshi and Y. Liu and E. Klampfl and P. MacNeille},
  year         = 2016,
  month        = {Dec},
  booktitle    = {2016 IEEE 16th International Conference on Data Mining (ICDM)},
  pages        = {1269--1274},
  doi          = {10.1109/icdm.2016.0171},
  keywords     = {Bayes methods;inference mechanisms;learning (artificial intelligence);pattern classification;Bayesian rule sets;MAP;UCI data sets;domain-specific interpretability definition;inference approach;interpretable classification;learning rule set models;Bayes methods;Computational modeling;Data mining;Data models;Predictive models;Search problems;Simulated annealing;Bayesian modeling;association rules;classifier;interpretable machine learning}
}
@article{wang2016towards,
  title        = {Towards bayesian deep learning: A survey},
  author       = {Wang, Hao and Yeung, Dit-Yan},
  year         = 2016,
  journal      = {arXiv preprint arXiv:1604.01662}
}
@inproceedings{wang2021swiftnet,
  title        = {Swiftnet: Real-time video object segmentation},
  author       = {Wang, Haochen and Jiang, Xiaolong and Ren, Haibing and Hu, Yao and Bai, Song},
  year         = 2021,
  booktitle    = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages        = {1296--1305}
}
@inproceedings{wang2021uncertainty,
  title        = {Uncertainty-aware pseudo label refinery for domain adaptive semantic segmentation},
  author       = {Wang, Yuxi and Peng, Junran and Zhang, ZhaoXiang},
  year         = 2021,
  booktitle    = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages        = {9092--9101}
}
@article{wang2022internimage,
  title        = {Internimage: Exploring large-scale vision foundation models with deformable convolutions},
  author       = {Wang, Wenhai and Dai, Jifeng and Chen, Zhe and Huang, Zhenhang and Li, Zhiqi and Zhu, Xizhou and Hu, Xiaowei and Lu, Tong and Lu, Lewei and Li, Hongsheng and others},
  year         = 2022,
  journal      = {arXiv preprint arXiv:2211.05778}
}
@article{warburg20_bayes_tripl_loss,
  title        = {Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval},
  author       = {Warburg, Frederik and J{\o}rgensen, Martin and Civera, Javier and Hauberg, S{\o}ren},
  year         = 2020,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2011.12663v2},
  abstract     = {Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem. Current methods for estimating uncertainties are poorly calibrated, computationally expensive, or based on heuristics. We present a new method that views image embeddings as stochastic features rather than deterministic features. Our two main contributions are (1) a likelihood that matches the triplet constraint and that evaluates the probability of an anchor being closer to a positive than a negative; and (2) a prior over the feature space that justifies the conventional l2 normalization. To ensure computational efficiency, we derive a variational approximation of the posterior, called the Bayesian triplet loss, that produces state-of-the-art uncertainty estimates and matches the predictive performance of current state-of-the-art methods.},
  archiveprefix = {arXiv},
  eprint       = {2011.12663},
  primaryclass = {cs.CV}
}
@article{wei-2022-perfor-direc,
  title        = {On the Performance of Direct Loss Minimization for Bayesian Neural Networks},
  author       = {Wei, Yadi and Khardon, Roni},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2211.08393v1},
  abstract     = {Direct Loss Minimization (DLM) has been proposed as a pseudo-Bayesian method motivated as regularized loss minimization. Compared to variational inference, it replaces the loss term in the evidence lower bound (ELBO) with the predictive log loss, which is the same loss function used in evaluation. A number of theoretical and empirical results in prior work suggest that DLM can significantly improve over ELBO optimization for some models. However, as we point out in this paper, this is not the case for Bayesian neural networks (BNNs). The paper explores the practical performance of DLM for BNN, the reasons for its failure and its relationship to optimizing the ELBO, uncovering some interesting facts about both algorithms.},
  archiveprefix = {arXiv},
  eprint       = {2211.08393},
  primaryclass = {cs.LG},
  file         = {/home/ethan/org/pdfs/wei-2022-perfor-direc.pdf}
}
@article{welford1962note,
  title        = {Note on a method for calculating corrected sums of squares and products},
  author       = {Welford, BP},
  year         = 1962,
  journal      = {Technometrics},
  publisher    = {Taylor \& Francis},
  volume       = 4,
  number       = 3,
  pages        = {419--420}
}
@inproceedings{welling2011bayesian,
  title        = {Bayesian learning via stochastic gradient Langevin dynamics},
  author       = {Welling, Max and Teh, Yee W},
  year         = 2011,
  booktitle    = {Proceedings of the 28th international conference on machine learning (ICML-11)},
  pages        = {681--688}
}
@article{welling2011sgld,
  title        = {Bayesian learning via stochastic gradient langevin dynamics},
  author       = {Welling, M and Teh, YW},
  year         = 2011,
  journal      = {Proceedings of the 28th International Conference on Machine Learning, ICML 2011},
  publisher    = {Proceedings of the 28th International Conference on Machine Learning, ICML 2011},
  pages        = {681--688}
}
@inproceedings{wen2018flipout,
  title        = {Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches},
  author       = {Yeming Wen and Paul Vicol and Jimmy Ba and Dustin Tran and Roger B. Grosse},
  year         = 2018,
  booktitle    = {6th International Conference on Learning Representations, {ICLR} 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings},
  publisher    = {OpenReview.net},
  url          = {https://openreview.net/forum?id=rJNpifWAb},
  bibsource    = {dblp computer science bibliography, https://dblp.org},
  biburl       = {https://dblp.org/rec/conf/iclr/WenVBTG18.bib},
  timestamp    = {Thu, 25 Jul 2019 01:00:00 +0200}
}
@inproceedings{wenzel2020good,
  title        = {How Good is the Bayes Posterior in Deep Neural Networks Really?},
  author       = {Florian Wenzel and Kevin Roth and Bastiaan S. Veeling and Jakub Swiatkowski and Linh Tran and Stephan Mandt and Jasper Snoek and Tim Salimans and Rodolphe Jenatton and Sebastian Nowozin},
  year         = 2020,
  booktitle    = {International Conference of Machine Learning}
}
@inproceedings{williams1997computing,
  title        = {Computing with infinite networks},
  author       = {Williams, Christopher KI},
  year         = 1997,
  booktitle    = {Advances in neural information processing systems},
  pages        = {295--301}
}
@book{williamson2005,
  title        = {Bayesian Nets and Causality},
  author       = {Williamson, Jon},
  year         = 2005,
  publisher    = {Oxford University Press},
  address      = {Oxford}
}
@article{wilson1984,
  title        = {Variance Reduction Techniques for Digital Simulation},
  author       = {James R. Wilson},
  year         = 1984,
  journal      = {American Journal of Mathematical and Management Sciences},
  publisher    = {Taylor \& Francis},
  volume       = 4,
  number       = {3-4},
  pages        = {277--312},
  doi          = {10.1080/01966324.1984.10737147},
  url          = {https://doi.org/10.1080/01966324.1984.10737147},
  eprint       = {https://doi.org/10.1080/01966324.1984.10737147}
}
@inproceedings{wilson2016deep,
  title        = {Deep kernel learning},
  author       = {Wilson, Andrew Gordon and Hu, Zhiting and Salakhutdinov, Ruslan and Xing, Eric P},
  year         = 2016,
  booktitle    = {Artificial intelligence and statistics},
  pages        = {370--378},
  organization = {PMLR}
}
@article{wilson2020bayesian,
  title        = {Bayesian deep learning and a probabilistic perspective of generalization},
  author       = {Wilson, Andrew Gordon and Izmailov, Pavel},
  year         = 2020,
  journal      = {arXiv preprint arXiv:2002.08791}
}
@article{wu2017generalized,
  title        = {Generalized bouncy particle sampler},
  author       = {Wu, Changye and Robert, Christian P},
  year         = 2017,
  month        = {Jun},
  journal      = {arXiv preprint arXiv:1706.04781},
  pages        = {arXiv:1706.04781},
  keywords     = {Statistics - Computation},
  eid          = {arXiv:1706.04781},
  archiveprefix = {arXiv},
  eprint       = {1706.04781},
  primaryclass = {stat.CO},
  adsurl       = {https://ui.adsabs.harvard.edu/\#abs/2017arXiv170604781W},
  adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{wudeterministic,
  title        = {Deterministic Variational Inference for Robust Bayesian Neural Networks},
  author       = {Wu, Anqi and Nowozin, Sebastian and Meeds, Edward and Turner, Richard E and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel and Gaunt, Alexander L},
  year         = 2019,
  booktitle    = {International Conference on Learning Representations}
}
@article{xiao2017fashion,
  title        = {Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms},
  author       = {Xiao, Han and Rasul, Kashif and Vollgraf, Roland},
  year         = 2017,
  journal      = {arXiv preprint arXiv:1708.07747}
}
@article{xu2022pidnet,
  title        = {Pidnet: A real-time semantic segmentation network inspired from pid controller},
  author       = {Xu, Jiacong and Xiong, Zixiang and Bhattacharyya, Shankar P},
  year         = 2022,
  journal      = {arXiv preprint arXiv:2206.02066}
}
@article{yosinski2015understanding,
  title        = {Understanding neural networks through deep visualization},
  author       = {Yosinski, Jason and Clune, Jeff and Nguyen, Anh and Fuchs, Thomas and Lipson, Hod},
  year         = 2015,
  journal      = {CoRR},
  eprint       = {1506.06579},
  archiveprefix = {arXiv}
}
@article{yu-2018-bisen,
  title        = {Bisenet: Bilateral Segmentation Network for Real-Time Semantic Segmentation},
  author       = {Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
  year         = 2018,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1808.00897v1},
  abstract     = {Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048x1024 input, we achieve 68.4 \% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.},
  archiveprefix = {arXiv},
  eprint       = {1808.00897},
  primaryclass = {cs.CV},
  file         = {/home/ethan/org/pdfs/yu-2018-bisen.pdf}
}
@article{yu-2020-bisen-v2,
  title        = {Bisenet V2: Bilateral Network With Guided Aggregation for Real-Time Semantic Segmentation},
  author       = {Yu, Changqian and Gao, Changxin and Wang, Jingbo and Yu, Gang and Shen, Chunhua and Sang, Nong},
  year         = 2020,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2004.02147v1},
  abstract     = {The low-level details and high-level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sacrifice the low-level details, which leads to a considerable accuracy decrease. We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for realtime semantic segmentation. To this end, we propose an efficient and effective architecture with a good trade-off between speed and accuracy, termed Bilateral Segmentation Network (BiSeNet V2). This architecture involves: (i) a Detail Branch, with wide channels and shallow layers to capture low-level details and generate high-resolution feature representation; (ii) a Semantic Branch, with narrow channels and deep layers to obtain high-level semantic context. The Semantic Branch is lightweight due to reducing the channel capacity and a fast-downsampling strategy. Furthermore, we design a Guided Aggregation Layer to enhance mutual connections and fuse both types of feature representation. Besides, a booster training strategy is designed to improve the segmentation performance without any extra inference cost. Extensive quantitative and qualitative evaluations demonstrate that the proposed architecture performs favourably against a few state-of-the-art real-time semantic segmentation approaches. Specifically, for a 2,048x1,024 input, we achieve 72.6 \% Mean IoU on the Cityscapes test set with a speed of 156 FPS on one NVIDIA GeForce GTX 1080 Ti card, which is significantly faster than existing methods, yet we achieve better segmentation accuracy.},
  archiveprefix = {arXiv},
  eprint       = {2004.02147},
  primaryclass = {cs.CV},
  file         = {/home/ethan/org/pdfs/yu-2020-bisen-v2.pdf}
}
@inproceedings{yuan2019segmentation,
  title        = {Segmentation transformer: Object-contextual representations for semantic segmentation},
  author       = {Yuan, Yuhui and Chen, Xiaokang and Chen, Xilin and Wang, Jingdong},
  year         = 2020,
  book         = {ECCV}
}
@inproceedings{zeiler2011,
  title        = {Adaptive deconvolutional networks for mid and high level feature learning},
  author       = {M. D. Zeiler and G. W. Taylor and R. Fergus},
  year         = 2011,
  month        = {Nov},
  booktitle    = {2011 International Conference on Computer Vision},
  pages        = {2018--2025},
  doi          = {10.1109/iccv.2011.6126474},
  issn         = {1550-5499},
  keywords     = {deconvolution;feature extraction;image classification;image representation;inference mechanisms;learning (artificial intelligence);Caltech-101 datasets;Caltech-256 datasets;adaptive deconvolutional networks;classifier;complete objects;convolutional sparse coding;feature extraction;hierarchical model;high level feature learning;high-level object parts;image decompositions;inference scheme;low-level edges;max pooling;mid level feature learning;mid-level edge junctions;natural images;Adaptation models;Computational modeling;Deconvolution;Image reconstruction;Mathematical model;Switches;Training}
}
@inproceedings{zeiler2013stochastic,
  title        = {Stochastic Pooling for Regularization of Deep Convolutional Neural Networks},
  author       = {Matthew D. Zeiler and Rob Fergus},
  year         = 2013,
  booktitle    = {1st International Conference on Learning Representations, {ICLR} 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Conference Track Proceedings},
  url          = {http://arxiv.org/abs/1301.3557},
  editor       = {Yoshua Bengio and Yann LeCun},
  timestamp    = {Thu, 04 Apr 2019 13:20:07 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1301-3557.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{zeiler2014visualizing,
  title        = {Visualizing and understanding convolutional networks},
  author       = {Zeiler, Matthew D and Fergus, Rob},
  year         = 2014,
  booktitle    = {European conference on computer vision},
  pages        = {818--833},
  organization = {Springer}
}
@article{zhang-2017-mixup,
  title        = {Mixup: Beyond Empirical Risk Minimization},
  author       = {Zhang, Hongyi and Cisse, Moustapha and Dauphin, Yann N. and Lopez-Paz, David},
  year         = 2017,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/1710.09412v2},
  abstract     = {Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.},
  archiveprefix = {arXiv},
  eprint       = {1710.09412v2},
  primaryclass = {cs.LG}
}
@article{zhang-2022-bench-robus,
  title        = {Benchmarking the Robustness of Deep Neural Networks To Common Corruptions in Digital Pathology},
  author       = {Zhang, Yunlong and Sun, Yuxuan and Li, Honglin and Zheng, Sunyi and Zhu, Chenglu and Yang, Lin},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.14973v1},
  abstract     = {When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted images are generated by injecting nine types of common corruptions into validation images. Besides, two classification and one ranking metrics are designed to evaluate the prediction and confidence performance under corruption. Evaluated on two resulting benchmark datasets, we find that (1) a variety of deep neural network models suffer from a significant accuracy decrease (double the error on clean images) and the unreliable confidence estimation on corrupted images; (2) A low correlation between the validation and test errors while replacing the validation set with our benchmark can increase the correlation. Our codes are available on https://github.com/superjamessyx/robustness\_benchmark.},
  archiveprefix = {arXiv},
  eprint       = {2206.14973},
  primaryclass = {cs.CV},
  file         = {~/org/pdfs/zhang-2022-bench-robus.pdf}
}
@article{zhang-2022-low-precis,
  title        = {Low-Precision Stochastic Gradient Langevin Dynamics},
  author       = {Zhang, Ruqi and Wilson, Andrew Gordon and Sa, Christopher De},
  year         = 2022,
  journal      = {CoRR},
  url          = {http://arxiv.org/abs/2206.09909v1},
  abstract     = {While low-precision optimization has been widely used to accelerate deep learning, low-precision sampling remains largely unexplored. As a consequence, sampling is simply infeasible in many large-scale scenarios, despite providing remarkable benefits to generalization and uncertainty estimation for neural networks. In this paper, we provide the first study of low-precision Stochastic Gradient Langevin Dynamics (SGLD), showing that its costs can be significantly reduced without sacrificing performance, due to its intrinsic ability to handle system noise. We prove that the convergence of low-precision SGLD with full-precision gradient accumulators is less affected by the quantization error than its SGD counterpart in the strongly convex setting. To further enable low-precision gradient accumulators, we develop a new quantization function for SGLD that preserves the variance in each update step. We demonstrate that low-precision SGLD achieves comparable performance to full-precision SGLD with only 8 bits on a variety of deep learning tasks.},
  archiveprefix = {arXiv},
  eprint       = {2206.09909},
  primaryclass = {cs.LG},
  file         = {~/org/pdfs/zhang-2022-low-precis.pdf}
}
@article{zheng2021rectifying,
  title        = {Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation},
  author       = {Zheng, Zhedong and Yang, Yi},
  year         = 2021,
  journal      = {International Journal of Computer Vision},
  publisher    = {Springer},
  volume       = 129,
  number       = 4,
  pages        = {1106--1120}
}
@inproceedings{zhou2016learning,
  title        = {Learning deep features for discriminative localization},
  author       = {Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
  year         = 2016,
  booktitle    = {Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages        = {2921--2929}
}
@inproceedings{zhou2017scene,
  title        = {Scene Parsing through ADE20K Dataset},
  author       = {Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
  year         = 2017,
  booktitle    = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}
}
@article{zhou2022uncertainty,
  title        = {Uncertainty-aware consistency regularization for cross-domain semantic segmentation},
  author       = {Zhou, Qianyu and Feng, Zhengyang and Gu, Qiqi and Cheng, Guangliang and Lu, Xuequan and Shi, Jianping and Ma, Lizhuang},
  year         = 2022,
  journal      = {Computer Vision and Image Understanding},
  publisher    = {Elsevier},
  volume       = 221,
  pages        = 103448
}
@inproceedings{liu2022convnet,
  title={A convnet for the 2020s},
  author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11976--11986},
  year={2022}
}
@inproceedings{woo2018cbam,
  title={Cbam: Convolutional block attention module},
  author={Woo, Sanghyun and Park, Jongchan and Lee, Joon-Young and Kweon, In So},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={3--19},
  year={2018}
}
@article{guo2022attention,
  title={Attention mechanisms in computer vision: A survey},
  author={Guo, Meng-Hao and Xu, Tian-Xing and Liu, Jiang-Jiang and Liu, Zheng-Ning and Jiang, Peng-Tao and Mu, Tai-Jiang and Zhang, Song-Hai and Martin, Ralph R and Cheng, Ming-Ming and Hu, Shi-Min},
  journal={Computational Visual Media},
  volume={8},
  number={3},
  pages={331--368},
  year={2022},
  publisher={Springer}
}
@article{guo2022beyond,
  title={Beyond self-attention: External attention using two linear layers for visual tasks},
  author={Guo, Meng-Hao and Liu, Zheng-Ning and Mu, Tai-Jiang and Hu, Shi-Min},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}
@article{khan2022transformers,
  title={Transformers in vision: A survey},
  author={Khan, Salman and Naseer, Muzammal and Hayat, Munawar and Zamir, Syed Waqas and Khan, Fahad Shahbaz and Shah, Mubarak},
  journal={ACM computing surveys (CSUR)},
  volume={54},
  number={10s},
  pages={1--41},
  year={2022},
  publisher={ACM New York, NY}
}
@article{abnar2020quantifying,
  title={Quantifying attention flow in transformers},
  author={Abnar, Samira and Zuidema, Willem},
  journal={arXiv preprint arXiv:2005.00928},
  year={2020}
}
@online{gildenblat2021explore,
  author = {Jacob Gildenblat},
  title = {Exploring Explainability for Vision Transformers},
  year = 2021,
  url = {https://jacobgil.github.io/deeplearning/vision-transformer-explainability#attention-rollout},
  urldate = {2023-05-30}
}
@Misc{ucidatasets,
  author = {D.J. Newman and S. Hettich and C.L. Blake and C.J. Merz},
  year = {1998},
  title = {UCI Repository of machine learning databases},
  url = {http://www.ics.uci.edu/~mlearn/MLRepository.html},
  institution = {University of California, Irvine, Dept. of
    Information and Computer Sciences},
}
@inproceedings{howard2019searching,
  title={Searching for mobilenetv3},
  author={Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and others},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={1314--1324},
  year={2019}
}
@article{efron1986isn,
  title={Why isn't everyone a Bayesian?},
  author={Efron, Bradley},
  journal={The American Statistician},
  volume={40},
  number={1},
  pages={1--5},
  year={1986},
  publisher={Taylor \& Francis}
}
@article{goodman1999toward,
  title={Toward evidence-based medical statistics. 1: The P value fallacy},
  author={Goodman, Steven N},
  journal={Annals of internal medicine},
  volume={130},
  number={12},
  pages={995--1004},
  year={1999},
  publisher={American College of Physicians}
}
@inproceedings{goodman2008dirty,
  title={A dirty dozen: twelve p-value misconceptions},
  author={Goodman, Steven},
  booktitle={Seminars in hematology},
  volume={45},
  number={3},
  pages={135--140},
  year={2008},
  organization={Elsevier}
}
@article{gelman2008objections,
  title={Objections to Bayesian statistics},
  author={Gelman, Andrew},
  year={2008}
}
