Summaries of papers I have read during my time as a PhD student.
The /commented_pdfs folder contains pdfs with comments, highlights etc. (visible at least in Okular on Ubuntu) for all papers.
- All Papers
- Uncertainty Estimation
- Theoretical Properties of Deep Learning
- VAEs
- Normalizing Flows
- Autonomous Driving
- Medical Imaging
- Object Detection
- 3D Object Detection
- Visual Tracking
- Sequence Modeling
- Reinforcement Learning
- SysCon Deep Learning Reading Group
- SysCon Monte Carlo Reading Group
- Papers by Year
- NeurIPS
- ICML
- ICLR
- CVPR
- ECCV
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments (TODO!)]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [pdf] [pdf with comments] [comments]
- Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li
2019-07-08
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
- ATOM: Accurate Tracking by Overlap Maximization [pdf] [code] [pdf with comments] [comments]
- Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2018-11-19, CVPR2019
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
- Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
- Jacob Menick, Nal Kalchbrenner
2018-12-04, ICLR2019
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
- Auto-Encoding Variational Bayes [pdf] [pdf with comments (TODO!)] [comments (TOOD!)]
- Diederik P Kingma, Max Welling
2014-05-01, ICLR2014
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
- Language Models are Unsupervised Multitask Learners [pdf] [blog post] [code] [pdf with comments] [comments]
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
2019-02-14
- Predictive Uncertainty Estimation via Prior Networks [pdf] [pdf with comments] [comments]
- Andrey Malinin, Mark Gales
2018-02-28, NeurIPS2018
- Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS2019
- Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks [pdf] [pdf with comments] [comments]
- Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019-01-24
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
- A Simple Baseline for Bayesian Uncertainty in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
2019-02-07
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning [pdf] [code] [pdf with comments] [comments]
- Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2019-02-11
- Bayesian Dark Knowledge [pdf] [pdf with comments] [comments]
- Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
2015-06-07, NeurIPS2015
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [pdf] [pdf with comments] [comments]
- José Miguel Hernández-Lobato, Ryan P. Adams
2015-07-15, ICML2015
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
- Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
- Changyou Chen
2016-08-10
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [pdf] [code] [pdf with comments] [summary]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-04-19
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
- Neural Ordinary Differential Equations [pdf] [code] [slides] [pdf with comments] [summary]
- Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018-10-22, NeurIPS2018
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation [pdf] [pdf with comments] [summary]
- Jishnu Mukhoti, Yarin Gal
2018-11-30
- On Calibration of Modern Neural Networks [pdf] [code] [pdf with comments] [summary]
- Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017-08-03, ICML2017
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [pdf] [pdf with comments] [summary]
- Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
2018-06-07, ICML2018
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
- Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
2018-11-08
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
- Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
- Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
2018-10-12, AISTATS2019 submission
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors [pdf] [pdf with comments] [summary]
- Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, James Davidson
2018-07-24, ICML2018 Workshop
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
- On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
- Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
2018-05-29, NeurIPS2018
- Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
- Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
- Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks [pdf] [pdf with comments] [summary]
- Isidro Cortes-Ciriano, Andreas Bender
2018-09-24
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
- Lightweight Probabilistic Deep Networks [pdf] [pdf with comments] [summary]
- Jochen Gast, Stefan Roth
2018-05-29, CVPR2018
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
- Predictive Uncertainty Estimation via Prior Networks [pdf] [pdf with comments] [comments]
- Andrey Malinin, Mark Gales
2018-02-28, NeurIPS2018
- Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS2019
- A Simple Baseline for Bayesian Uncertainty in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
2019-02-07
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning [pdf] [code] [pdf with comments] [comments]
- Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2019-02-11
- Bayesian Dark Knowledge [pdf] [pdf with comments] [comments]
- Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
2015-06-07, NeurIPS2015
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [pdf] [pdf with comments] [comments]
- José Miguel Hernández-Lobato, Ryan P. Adams
2015-07-15, ICML2015
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
- Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
- Changyou Chen
2016-08-10
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation [pdf] [pdf with comments] [summary]
- Jishnu Mukhoti, Yarin Gal
2018-11-30
- On Calibration of Modern Neural Networks [pdf] [code] [pdf with comments] [summary]
- Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017-08-03, ICML2017
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
- Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
2018-11-08
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
- Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
- Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
2018-10-12, AISTATS2019 submission
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors [pdf] [pdf with comments] [summary]
- Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, James Davidson
2018-07-24, ICML2018 Workshop
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks [pdf] [pdf with comments] [summary]
- Isidro Cortes-Ciriano, Andreas Bender
2018-09-24
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
- Lightweight Probabilistic Deep Networks [pdf] [pdf with comments] [summary]
- Jochen Gast, Stefan Roth
2018-05-29, CVPR2018
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
- Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks [pdf] [pdf with comments] [comments]
- Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019-01-24
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
- Auto-Encoding Variational Bayes [pdf] [pdf with comments] [comments]
- Diederik P Kingma, Max Welling
2014-05-01, ICLR2014
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [pdf] [pdf with comments] [comments]
- Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li
2019-07-08
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation [pdf] [pdf with comments] [summary]
- Jishnu Mukhoti, Yarin Gal
2018-11-30
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [pdf] [pdf with comments] [comments]
- Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li
2019-07-08
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
- ATOM: Accurate Tracking by Overlap Maximization [pdf] [code] [pdf with comments] [comments]
- Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2018-11-19, CVPR2019
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [pdf] [code] [pdf with comments] [summary]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-04-19
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
- Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
2018-11-08
- Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
- Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
2018-10-12, AISTATS2019 submission
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks [pdf] [pdf with comments] [summary]
- Isidro Cortes-Ciriano, Andreas Bender
2018-09-24
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning [pdf] [code] [pdf with comments] [comments]
- Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2019-02-11
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
- Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
- Changyou Chen
2016-08-10
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
- Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
- Jacob Menick, Nal Kalchbrenner
2018-12-04, ICLR2019
- Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS2019
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
- Language Models are Unsupervised Multitask Learners [pdf] [blog post] [code] [pdf with comments] [comments]
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
2019-02-14
- Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks [pdf] [pdf with comments] [comments]
- Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019-01-24
- A Simple Baseline for Bayesian Uncertainty in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
2019-02-07
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [pdf] [code] [pdf with comments] [summary]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-04-19
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
- Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
- Neural Ordinary Differential Equations [pdf] [code] [slides] [pdf with comments] [summary]
- Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018-10-22, NeurIPS2018
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [pdf] [pdf with comments] [summary]
- Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
2018-06-07, ICML2018
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
- Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
- On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
- Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
2018-05-29, NeurIPS2018
- Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
- The Continuous-Discrete Time Feedback Particle Filter [pdf]
- Tao Yang, Henk A. P. Blom, Prashant G. Mehta
2014, American Control Conference
- Feedback Particle Filter [pdf]
- Tao Yang, Prashant G. Mehta, Sean P. Meyn
2013, IEEE Transactions on Automatic Control
- Markov Chains for Exploring Posterior Distributions [pdf] [pdf with comments]
- Luke Tierney
1994-12, The Annals of Statistics
- Particle Gibbs with Ancestor Sampling [pdf]
- Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
2014-06-14, Journal of Machine Learning Research
- Particle Markov chain Monte Carlo methods [pdf]
- Christophe Andrieu, Arnaud Doucet, Roman Holenstein
2010, Journal of the Royal Statistical Society
- State Space LSTM Models with Particle MCMC Inference [pdf]
- Xun Zheng, Manzil Zaheer, Amr Ahmed, Yuan Wang, Eric P Xing, Alexander J Smola
2017-11-30
- Rethinking the Effective Sample Size [pdf]
- Víctor Elvira, Luca Martino, Christian P. Robert
- `2018-09-11,
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
- Predictive Uncertainty Estimation via Prior Networks [pdf] [pdf with comments] [comments]
- Andrey Malinin, Mark Gales
2018-02-28, NeurIPS2018
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
- Neural Ordinary Differential Equations [pdf] [code] [slides] [pdf with comments] [summary]
- Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018-10-22, NeurIPS2018
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
- On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
- Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
2018-05-29, NeurIPS2018
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
- Bayesian Dark Knowledge [pdf] [pdf with comments] [comments]
- Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
2015-06-07, NeurIPS2015
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [pdf] [pdf with comments] [summary]
- Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
2018-06-07, ICML2018
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors [pdf] [pdf with comments] [summary]
- Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, James Davidson
2018-07-24, ICML2018 Workshop
- Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
- Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
- Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
- On Calibration of Modern Neural Networks [pdf] [code] [pdf with comments] [summary]
- Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017-08-03, ICML2017
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [pdf] [pdf with comments] [comments]
- José Miguel Hernández-Lobato, Ryan P. Adams
2015-07-15, ICML2015
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
- Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
- Jacob Menick, Nal Kalchbrenner
2018-12-04, ICLR2019
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
- Auto-Encoding Variational Bayes [pdf] [pdf with comments] [comments]
- Diederik P Kingma, Max Welling
2014-05-01, ICLR2014
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
- ATOM: Accurate Tracking by Overlap Maximization [pdf] [code] [pdf with comments] [comments]
- Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2018-11-19, CVPR2019
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
- Lightweight Probabilistic Deep Networks [pdf] [pdf with comments] [summary]
- Jochen Gast, Stefan Roth
2018-05-29, CVPR2018
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [pdf] [pdf with comments] [comments]
- Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li
2019-07-08
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
- Language Models are Unsupervised Multitask Learners [pdf] [blog post] [code] [pdf with comments] [comments]
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
2019-02-14
- Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS2019
- Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks [pdf] [pdf with comments] [comments]
- Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019-01-24
- A Simple Baseline for Bayesian Uncertainty in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
2019-02-07
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning [pdf] [code] [pdf with comments] [comments]
- Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2019-02-11
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
- ATOM: Accurate Tracking by Overlap Maximization [pdf] [code] [pdf with comments] [comments]
- Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2018-11-19, CVPR2019
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
- Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
- Jacob Menick, Nal Kalchbrenner
2018-12-04, ICLR2019
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
- Predictive Uncertainty Estimation via Prior Networks [pdf] [pdf with comments] [comments]
- Andrey Malinin, Mark Gales
2018-02-28, NeurIPS2018
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [pdf] [code] [pdf with comments] [summary]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-04-19
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
- Neural Ordinary Differential Equations [pdf] [code] [slides] [pdf with comments] [summary]
- Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018-10-22, NeurIPS2018
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation [pdf] [pdf with comments] [summary]
- Jishnu Mukhoti, Yarin Gal
2018-11-30
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [pdf] [pdf with comments] [summary]
- Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
2018-06-07, ICML2018
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
- Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
2018-11-08
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
- Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
- Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
2018-10-12, AISTATS2019 submission
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors [pdf] [pdf with comments] [summary]
- Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, James Davidson
2018-07-24, ICML2018 Workshop
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
- On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
- Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
2018-05-29, NeurIPS2018
- Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
- Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
- Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks [pdf] [pdf with comments] [summary]
- Isidro Cortes-Ciriano, Andreas Bender
2018-09-24
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
- Lightweight Probabilistic Deep Networks [pdf] [pdf with comments] [summary]
- Jochen Gast, Stefan Roth
2018-05-29, CVPR2018
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
- On Calibration of Modern Neural Networks [pdf] [code] [pdf with comments] [summary]
- Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017-08-03, ICML2017
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
- Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
- Changyou Chen
2016-08-10
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
- Bayesian Dark Knowledge [pdf] [pdf with comments] [comments]
- Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
2015-06-07, NeurIPS2015
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [pdf] [pdf with comments] [comments]
- José Miguel Hernández-Lobato, Ryan P. Adams
2015-07-15, ICML2015
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
- Auto-Encoding Variational Bayes [pdf] [pdf with comments] [comments]
- Diederik P Kingma, Max Welling
2014-05-01, ICLR2014
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011