NIPS Submissions

Machine Learning Automation Toolbox (MLAUT)
Franz J. Kiraly, Viktor Kazakov
- Sep 30, 2018 (edited Oct 01, 2018)
- NIPS 2018 Workshop MLOSS Submission
- Readers: Everyone
Workshop contribution MLJ
Diego Arenas, Edoardo Barp, Gergö Bohner, Valentin Churvay, Franz Kiraly, Thibaut Lienart, Sebastian Vollmer, Mike Innes, Bernd Bischl
- Sep 30, 2018 (edited Oct 01, 2018)
- NIPS 2018 Workshop MLOSS Submission
- Readers: Everyone
Sherpa: Hyperparameter Optimization for Machine Learning Models
Lars Hertel, Julian Collado, Peter Sadowski, Pierre Baldi
- Sep 30, 2018 (edited Oct 01, 2018)
- NIPS 2018 Workshop MLOSS Submission
- Readers: Everyone
Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations
Priyam Parashar, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura
- Oct 31, 2017 (edited Jun 13, 2018)
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
A modern compiler infrastructure for deep learning systems with adjoint code generation in a domain-specific IR
Richard Wei, Lane Schwartz, Vikram Adve
- Oct 28, 2017 (edited Dec 07, 2017)
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone
End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies
Hesham M. Eraqi, Mohamed N. Moustafa, Jens Honer
- Oct 31, 2017 (edited Nov 22, 2017)
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
Motion and Appearance Based Multi-Task Learning Network for Autonomous Driving
Mennatullah Siam, Heba Mahgoub, Mohamed Zahran, Senthil Yogamani, Martin Jagersand, Ahmad El-Sallab
- Oct 31, 2017 (edited Nov 20, 2017)
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
withdraw
withdraw
- Oct 11, 2017 (edited Nov 11, 2017)
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
Automatic Differentiation Equipped Variable Elimination for Sensitivity Analysis on Probabilistic Inference Queries
Jeff Druce
- Nov 03, 2017
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone
Achieving linear or quadratic convergence on piecewise smooth optimization problems
Andreas Griewank, Andrea Walther
- Nov 03, 2017
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone
Deep occupancy maps: a continuous mapping technique for dynamic environments
Ransalu Senanayake, Thushan Ganegedara, Fabio Ramos
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
Sequence stacking using dual encoder Seq2Seq recurrent networks
Alessandro Bay, Biswa Sengupta
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
Xiangyu Yue, Bichen Wu, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
Vehicle Classification on Low-resolution and Occluded images: A low-cost labeled dataset for augmentation
Deepak Mittal, Mudamala Avinash Reddy, Gitakrishnan Ramadurai, Kaushik Mitra, Balaraman Ravindran
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
AutonoVi-Sim: Autonomous Vehicle Simulation Platform with Weather, Sensing, and Traffic control
Andrew Best, Sahil Narang, Lucas Pasqualin, Daniel Barber, Dinesh Manocha
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
CASNSC: A context-based approach for accurate pedestrian motion prediction at intersections
Nikita Japuria, Golnaz Habibi, Jonathan P. How
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
On the Real-Time Vehicle Placement Problem
Abhinav Jauhri, Carlee Joe-Wong, John Paul Shen
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
Tactical Decision Making for Lane Changing with Deep Reinforcement Learning
Mustafa Mukadam, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
Unified Transport Engine: Learning to Predict Traffic Congestions
Ramesh Sarukkai, Shaohui Sun
- Oct 31, 2017
- NIPS 2017 Workshop MLITS Submission
- Readers: Everyone
How automatic differentiation improves numerical weather forecasting and promising applications of gradient machine learning with automatic differentiation
Daniel Holdaway
- Oct 29, 2017
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone
End-to-end Training of Differentiable Pipelines Across Machine Learning Frameworks
Mitar Milutinovic, Atılım Güneş Baydin, Robert Zinkov, William Harvey, Dawn Song, Frank Wood, Wade Shen
- Oct 29, 2017 (edited Oct 29, 2017)
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone
An Overview of High Order Reverse Mode
Mu Wang, Alex Pothen
- Oct 28, 2017
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone
Automatic Differentiation in Myia
Olivier Breuleux, Bart van Merriënboer
- Oct 28, 2017
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone
Automatic Differentiation of Parallelised Convolutional Neural Networks - Lessons from Adjoint PDE Solvers
Jan Hückelheim, Paul Hovland
- Oct 28, 2017
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer
- Oct 28, 2017 (edited Oct 28, 2017)
- NIPS 2017 Workshop Autodiff Submission
- Readers: Everyone