@article{betavae,
title={beta-{VAE}: Learning Basic Visual Concepts with a Constrained Variational Framework},
author={Irina Higgins and Loic Matthey and Arka Pal and Christopher Burgess and Xavier Glorot and Matthew Botvinick and Shakir Mohamed and Alexander Lerchner},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=Sy2fzU9gl}
}

@article{tcvae,
  doi = {10.48550/ARXIV.1802.04942},
  
  url = {https://arxiv.org/abs/1802.04942},
  
  author = {Chen, Ricky T. Q. and Li, Xuechen and Grosse, Roger and Duvenaud, David},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Isolating Sources of Disentanglement in Variational Autoencoders},
  
  publisher = {arXiv},
  
  year = {2018},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@article{IntegratedGradients,
  title={Axiomatic attribution for deep networks},
  author={Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi},
  booktitle={International conference on machine learning},
  pages={3319--3328},
  year={2017},
  organization={PMLR}
}

@article{GradientShap,
  title={A unified approach to interpreting model predictions},
  author={Lundberg, Scott M and Lee, Su-In},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}


@article{densenet,
  doi = {10.48550/ARXIV.1608.06993},
  
  url = {https://arxiv.org/abs/1608.06993},
  
  author = {Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q.},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Densely Connected Convolutional Networks},
  
  publisher = {arXiv},
  
  year = {2016},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{BootstrapYourOwnLatent,
  author    = {Jean{-}Bastien Grill and
               Florian Strub and
               Florent Altch{\'{e}} and
               Corentin Tallec and
               Pierre H. Richemond and
               Elena Buchatskaya and
               Carl Doersch and
               Bernardo {\'{A}}vila Pires and
               Zhaohan Daniel Guo and
               Mohammad Gheshlaghi Azar and
               Bilal Piot and
               Koray Kavukcuoglu and
               R{\'{e}}mi Munos and
               Michal Valko},
  title     = {Bootstrap Your Own Latent: {A} New Approach to Self-Supervised Learning},
  journal   = {CoRR},
  volume    = {abs/2006.07733},
  year      = {2020},
  url       = {https://arxiv.org/abs/2006.07733},
  eprinttype = {arXiv},
  eprint    = {2006.07733},
  timestamp = {Wed, 17 Jun 2020 14:28:54 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2006-07733.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{anomalydetectionexample,
	doi = {10.1007/jhep06(2021)161},
  
	url = {https://doi.org/10.1007\%2Fjhep06\%282021\%29161"},
  
	year = 2021,
	month = {6},
  
	publisher = {Springer Science and Business Media {LLC}
},
  
	volume = {2021},
  
	number = {6},
  
	author = {Thorben Finke and Michael Krämer and Alessandro Morandini and Alexander Mück and Ivan Oleksiyuk},
  
	title = {Autoencoders for unsupervised anomaly detection in high energy physics},
  
	journal = {Journal of High Energy Physics}
}

@article{clusteringautoencoders,
  doi = {10.48550/ARXIV.2102.07472},
  
  url = {https://arxiv.org/abs/2102.07472},
  
  author = {Lu, Si and Li, Ruisi},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {Creative Commons Attribution 4.0 International}
}

@article{saliency,
  doi = {10.48550/ARXIV.1312.6034},
  
  url = {https://arxiv.org/abs/1312.6034},
  
  author = {Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps},
  
  publisher = {arXiv},
  
  year = {2013},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{lime,
  doi = {10.48550/ARXIV.1602.04938},
  
  url = {https://arxiv.org/abs/1602.04938},
  
  author = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {"Why Should I Trust You?": Explaining the Predictions of Any Classifier},
  
  publisher = {arXiv},
  
  year = {2016},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}


@InProceedings{integratedgrads,
  title = 	 {Axiomatic Attribution for Deep Networks},
  author =       {Mukund Sundararajan and Ankur Taly and Qiqi Yan},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {3319--3328},
  year = 	 {2017},
  editor = 	 {Precup, Doina and Teh, Yee Whye},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--11 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/sundararajan17a/sundararajan17a.pdf},
  url = 	 {https://proceedings.mlr.press/v70/sundararajan17a.html},
  abstract = 	 {We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. We identify two fundamental axioms—Sensitivity and Implementation Invariance that attribution methods ought to satisfy. We show that they are not satisfied by most known attribution methods, which we consider to be a fundamental weakness of those methods. We use the axioms to guide the design of a new attribution method called Integrated Gradients. Our method requires no modification to the original network and is extremely simple to implement; it just needs a few calls to the standard gradient operator. We apply this method to a couple of image models, a couple of text models and a chemistry model, demonstrating its ability to debug networks, to extract rules from a network, and to enable users to engage with models better.}
}

@article{dknn,
  doi = {10.48550/ARXIV.1803.04765},
  
  url = {https://arxiv.org/abs/1803.04765},
  
  author = {Papernot, Nicolas and McDaniel, Patrick},
  
  keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning},
  
  publisher = {arXiv},
  
  year = {2018},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{tracIn,
 author = {Pruthi, Garima and Liu, Frederick and Kale, Satyen and Sundararajan, Mukund},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {19920--19930},
 publisher = {Curran Associates, Inc.},
 title = {Estimating Training Data Influence by Tracing Gradient Descent},
 url = {https://proceedings.neurips.cc/paper/2020/file/e6385d39ec9394f2f3a354d9d2b88eec-Paper.pdf},
 volume = {33},
 year = {2020}
}

@article{simplex,
  doi = {10.48550/ARXIV.2110.15355},
  
  url = {https://arxiv.org/abs/2110.15355},
  
  author = {Crabbé, Jonathan and Qian, Zhaozhi and Imrie, Fergus and van der Schaar, Mihaela},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Human-Computer Interaction (cs.HC), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Explaining Latent Representations with a Corpus of Examples},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{originalpaper,
  doi = {10.48550/ARXIV.2203.01928},
  
  url = {https://arxiv.org/abs/2203.01928},
  
  author = {Crabbé, Jonathan and van der Schaar, Mihaela},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Label-Free Explainability for Unsupervised Models},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}

@article{koh2017,
  title={Understanding black-box predictions via influence functions},
  author={Koh, Pang Wei and Liang, Percy},
  booktitle={International conference on machine learning},
  pages={1885--1894},
  year={2017},
  organization={PMLR}
}
@article{simclrref,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}
@inproceedings{resnetref,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
@article{mnist,
    author={LeCun, Yann and Bottou, L and Bengio, Y and Haffner, P},
    title={Gradient-based learning applied to document recognition},
    journal={Proceedings of the IEEE},
    volume="86",
    number="11",
    pages="2278---2323",
    year="1998",
    DOI={10.1109/5.726791}
}
@article{ecg,
    author={Goldberg, A L and Amaral, L A and Glass, L and Hausdorff, J M and Ivanov, P C and Mark, R G and Mietus, J E and Moody, G B and Peng, C K and Stanley, H E},
    title={PhysioBank, PhysioToolkit and PhysioNet: components of a new research resource for complex physiologic signals},
    journal={Circulation},
    volume="101",
    number="23",
    year="2000",
    DOI={10.1161/01.CIR.101.23.E215}
}
@article{cifar10,
    author={Krizhevsky, A},
    title={Learning multiple layers of features from tiny images},
    series={Technical report},
    year="2009"
}
@article{dsprites,
    author={Matthey, L and Higgins, I and Hassabis, D and Lerchner, A},
    title={dSprites: Disentanglement testing Sprites dataset},
    note={\url{https://github.com/deepmind/dsprites-dataset/}},
    year="2017"

}
@article{fashion,
  title={Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms},
  author={Xiao, Han and Rasul, Kashif and Vollgraf, Roland},
  journal={arXiv preprint arXiv:1708.07747},
  year={2017}
}

@article{Montavon,
  author    = {Gr{\'{e}}goire Montavon and
               Wojciech Samek and
               Klaus{-}Robert M{\"{u}}ller},
  title     = {Methods for Interpreting and Understanding Deep Neural Networks},
  journal   = {CoRR},
  volume    = {abs/1706.07979},
  year      = {2017},
  url       = {http://arxiv.org/abs/1706.07979},
  eprinttype = {arXiv},
  eprint    = {1706.07979},
  timestamp = {Mon, 13 Aug 2018 16:48:40 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/MontavonSM17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{cook1980ch,
  title={Characterizations of an empirical influence function for detecting influential cases in regression},
  author={Cook, R Dennis and Weisberg, Sanford},
  journal={Technometrics},
  volume={22},
  number={4},
  pages={495--508},
  year={1980},
  publisher={Taylor \& Francis}
}

@inproceedings{koh,
  title={Understanding black-box predictions via influence functions},
  author={Koh, Pang Wei and Liang, Percy},
  booktitle={International conference on machine learning},
  pages={1885--1894},
  year={2017},
  organization={PMLR}
}

@article{pruthi,
  title={Estimating training data influence by tracing gradient descent},
  author={Pruthi, Garima and Liu, Frederick and Kale, Satyen and Sundararajan, Mukund},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={19920--19930},
  year={2020}
}

@article{crabbe,
  title={Explaining latent representations with a corpus of examples},
  author={Crabb{\'e}, Jonathan and Qian, Zhaozhi and Imrie, Fergus and van der Schaar, Mihaela},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={12154--12166},
  year={2021}
}
@article{papernot,
  title={Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning},
  author={Papernot, Nicolas and McDaniel, Patrick},
  journal={arXiv preprint arXiv:1803.04765},
  year={2018}
}