Coder Social home page Coder Social logo

papers's Introduction

About

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.

Index

All Papers:

Papers Read in 2020:

[20-03-09] [paper92]
[20-02-28] [paper91]
[20-02-21] [paper90]
[20-02-18] [paper89]
[20-02-15] [paper88]
[20-02-14] [paper87]
[20-02-13] [paper86]
[20-02-08] [paper85]
[20-01-31] [paper84]
[20-01-24] [paper83]
[20-01-20] [paper82]
[20-01-17] [paper81]
[20-01-16] [paper80]
[20-01-15] [paper79]
[20-01-14] [paper78]
[20-01-10] [paper77]
[20-01-08] [paper76]
[20-01-06] [paper75]

Papers Read in 2019:

[19-12-22] [paper74]
[19-12-20] [paper73]
[19-12-20] [paper72]
[19-12-19] [paper71]
[19-12-15] [paper70]
[19-12-14] [paper69]
[19-12-13] [paper68]
[19-11-29] [paper67]
[19-11-26] [paper66]
[19-11-22] [paper65]
[19-10-28] [paper64]
[19-10-18] [paper63]
  • Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
  • Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
  • 2016-06-15, NeurIPS2016
[19-10-11] [paper62]
[19-10-04] [paper61]
[19-07-11] [paper60]
  • 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
[19-07-10] [paper59]
[19-07-03] [paper58]
[19-06-12] [paper57]
[19-06-12] [paper56]
[19-06-05] [paper55]
  • 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
[19-05-29] [paper54]
  • 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
[19-04-05] [paper53]
  • 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)
[19-03-29] [paper52]
  • Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
  • Jacob Menick, Nal Kalchbrenner
  • 2018-12-04, ICLR2019
[19-03-15] [paper51]
[19-03-11] [paper50]
[19-03-04] [paper49]
[19-03-01] [paper48]
[19-02-27] [paper47]
[19-02-25] [paper46]
  • 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
[19-02-22] [paper45]
  • 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
[19-02-17] [paper44]
[19-02-14] [paper43]
[19-02-13] [paper42]
[19-02-12] [paper41]
[19-02-07] [paper40]
[19-02-06] [paper39]
  • 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
[19-02-05] [paper38]
[19-01-28] [paper37]
[19-01-27] [paper36]
[19-01-26] [paper35]
  • 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
[19-01-25] [paper34]
[19-01-25] [paper33]
[19-01-24] [paper32]
  • Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
  • Changyou Chen
  • 2016-08-10
[19-01-24] [paper31]
[19-01-23] [paper30]
[19-01-23] [paper29]
[19-01-17] [paper28]
[19-01-09] [paper27]
  • 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

Papers Read in 2018:

[18-12-12] [paper26]
[18-12-06] [paper25]
[18-12-05] [paper24]
[18-11-29] [paper23]
[18-11-22] [paper22]
  • 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
[18-11-22] [paper21]
  • 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
[18-11-16] [paper20]
  • 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
[18-11-15] [paper19]
  • 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
[18-11-12] [paper18]
  • Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
  • Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
  • 2018-11-08
[18-11-08] [paper17]
[18-10-26] [paper16]
  • 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
[18-10-25] [paper15]
  • 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
[18-10-19] [paper14]
  • Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
  • Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
  • 2018-10-12, AISTATS2019 submission
[18-10-18] [paper13]
  • Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
  • Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
  • 2017-11-17, NeurIPS2017
[18-10-18] [paper12]
  • 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
[18-10-05] [paper11]
[18-10-04] [paper10]
[18-10-04] [paper9]
  • On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
  • Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
  • 2018-05-29, NeurIPS2018
[18-09-30] [paper8]
  • 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
[18-09-27] [paper7]
  • 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
[18-09-27] [paper6]
[18-09-25] [paper5]
  • 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
[18-09-25] [paper4]
  • 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
[18-09-24] [paper3]
[18-09-24] [paper2]
[18-09-20] [paper1]
  • 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

Uncertainty Estimation:

[20-02-28] [paper91]
[20-02-13] [paper86]
[20-02-08] [paper85]
[20-01-31] [paper84]
[20-01-08] [paper76]
[19-06-12] [paper56]
[19-06-05] [paper55]
  • 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
[19-04-05] [paper53]
  • 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)
[19-02-27] [paper47]
[19-02-25] [paper46]
  • 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
[19-02-14] [paper43]
[19-02-13] [paper42]
[19-02-12] [paper41]
[19-02-07] [paper40]
[19-02-06] [paper39]
  • 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
[19-02-05] [paper38]
[19-01-28] [paper37]
[19-01-27] [paper36]
[19-01-26] [paper35]
  • 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
[19-01-25] [paper34]
[19-01-25] [paper33]
[19-01-24] [paper32]
  • Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
  • Changyou Chen
  • 2016-08-10
[19-01-23] [paper30]
[19-01-23] [paper29]
[19-01-09] [paper27]
  • 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
[18-12-06] [paper25]
[18-12-05] [paper24]
[18-11-29] [paper23]
[18-11-22] [paper22]
  • 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
[18-11-16] [paper20]
  • 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
[18-11-12] [paper18]
  • Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
  • Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
  • 2018-11-08
[18-10-26] [paper16]
  • 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
[18-10-19] [paper14]
  • Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
  • Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
  • 2018-10-12, AISTATS2019 submission
[18-10-18] [paper13]
  • Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
  • Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
  • 2017-11-17, NeurIPS2017
[18-10-18] [paper12]
  • 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
[18-09-25] [paper5]
  • 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
[18-09-25] [paper4]
  • 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
[18-09-24] [paper3]
[18-09-24] [paper2]

Theoretical Properties of Deep Learning:

[20-03-09] [paper92]
[20-01-24] [paper83]
[20-01-17] [paper81]
[19-02-22] [paper45]
  • 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
[19-02-17] [paper44]
[19-01-17] [paper28]
[18-11-08] [paper17]
[18-10-25] [paper15]
  • 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
[18-09-20] [paper1]
  • 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

VAEs:

[20-02-14] [paper87]
[20-01-10] [paper77]
[19-11-26] [paper66]
[19-03-11] [paper50]
[19-03-04] [paper49]

Normalizing Flows:

[19-12-20] [paper72]
[19-11-26] [paper66]
[19-10-18] [paper63]
  • Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
  • Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
  • 2016-06-15, NeurIPS2016
[19-10-11] [paper62]
[18-09-27] [paper6]

Autonomous Driving:

[19-07-11] [paper60]
  • 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
[19-07-10] [paper59]
[19-07-03] [paper58]
[19-06-05] [paper55]
  • 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
[19-01-09] [paper27]
  • 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
[18-12-06] [paper25]
[18-11-22] [paper22]
  • 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
[18-11-16] [paper20]
  • 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
[18-10-26] [paper16]
  • 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
[18-10-05] [paper11]
[18-10-04] [paper10]
[18-09-25] [paper4]
  • 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
[18-09-24] [paper2]

Medical Imaging:

[18-11-22] [paper22]
  • 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

Object Detection:

[19-07-03] [paper58]
[19-06-12] [paper56]

3D Object Detection:

[19-07-11] [paper60]
  • 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
[19-07-10] [paper59]
[19-07-03] [paper58]
[19-06-05] [paper55]
  • 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
[18-10-26] [paper16]
  • 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
[18-10-05] [paper11]
[18-10-04] [paper10]
[18-09-25] [paper4]
  • 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

3D Multi-Object Tracking:

[20-02-18] [paper89]
[20-02-15] [paper88]

Visual Tracking:

[19-06-12] [paper57]

Sequence Modeling:

[20-01-17] [paper81]
[20-01-10] [paper77]
[19-11-26] [paper66]
[19-10-04] [paper61]
[19-05-29] [paper54]
  • 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
[19-03-15] [paper51]
[19-01-24] [paper31]
[18-11-22] [paper21]
  • 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

Reinforcement Learning:

[20-02-13] [paper86]
[20-02-08] [paper85]
[19-11-29] [paper67]
[19-11-22] [paper65]
[19-02-05] [paper38]

System Identification:

[19-11-26] [paper66]
[19-10-28] [paper64]

Energy-Based Models:

[20-01-20] [paper82]
[20-01-16] [paper80]
[20-01-15] [paper79]
[20-01-14] [paper78]
[20-01-06] [paper75]
[19-12-22] [paper74]
[19-12-20] [paper73]
[19-12-20] [paper72]
[19-12-19] [paper71]
[19-12-15] [paper70]
[19-12-14] [paper69]
[19-12-13] [paper68]

Ensembling:

[20-02-28] [paper91]
[19-02-05] [paper38]
[18-11-16] [paper20]
  • 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
[18-11-12] [paper18]
  • Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
  • Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
  • 2018-11-08
[18-10-19] [paper14]
  • Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
  • Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
  • 2018-10-12, AISTATS2019 submission
[18-10-18] [paper13]
  • Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
  • Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
  • 2017-11-17, NeurIPS2017
[18-09-25] [paper5]
  • 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 MCMC:

[19-04-05] [paper53]
  • 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)
[19-02-13] [paper42]
[19-01-26] [paper35]
  • 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
[19-01-25] [paper34]
[19-01-25] [paper33]
[19-01-24] [paper32]
  • Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
  • Changyou Chen
  • 2016-08-10
[19-01-23] [paper30]
[19-01-23] [paper29]

Variational Inference:

[20-01-08] [paper76]
[19-02-07] [paper40]
[19-01-28] [paper37]
[19-01-27] [paper36]

Neural Processes:

[20-02-21] [paper90]
[18-09-30] [paper8]
  • 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
[18-09-27] [paper7]
  • 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

SysCon Deep Learning Reading Group:

Reading Group Papers in 2020:

[2020 w.10] [20-03-09] [paper92]
[2020 w.9] [20-02-28] [paper91]
[2020 w.8] [20-02-21] [paper90]
[2020 w.7] [20-02-13] [paper86]
[2020 w.6] [20-02-08] [paper85]
[2020 w.5] [20-01-31] [paper84]
[2020 w.4] [20-01-24] [paper83]
[2020 w.3] [20-01-17] [paper81]
[2020 w.2] [20-01-10] [paper77]

Reading Group Papers in 2019:

[2019 w.48] [19-11-29] [paper67]
[2019 w.47] [19-11-22] [paper65]
[2019 w.46] [19-10-28] [paper64]
[2019 w.45] [19-01-27] [paper36]
[2019 w.43] [18-09-27] [paper6]
[2019 w.42] [19-10-18] [paper63]
  • Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
  • Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
  • 2016-06-15, NeurIPS2016
[2019 w.41] [19-10-11] [paper62]
[2019 w.40] [19-10-04] [paper61]
[2019 w.23] [19-06-05] [paper55]
  • 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
[2019 w.22] [19-05-29] [paper54]
  • 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
[2019 w.18] [19-02-17] [paper44]
[2019 w.14] [19-04-05] [paper53]
  • 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)
[2019 w.13] [19-03-29] [paper52]
  • Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
  • Jacob Menick, Nal Kalchbrenner
  • 2018-12-04, ICLR2019
[2019 w.12] [19-02-25] [paper46]
  • 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
[2019 w.11] [19-03-15] [paper51]
[2019 w.10] [19-03-04] [paper49]
[2019 w.9] [19-03-01] [paper48]
[2019 w.8] [19-02-22] [paper45]
  • 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
[2019 w.7] [19-02-14] [paper43]
[2019 w.6] [19-02-05] [paper38]
[2019 w.5] [19-01-25] [paper33]
[2019 w.4] [19-01-24] [paper31]
[2019 w.3] [19-01-17] [paper28]
[2019 w.2] [18-09-30] [paper8]
  • 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

Reading Group Papers in 2018:

[2018 w.50] [18-12-12] [paper26]
[2018 w.49] [18-11-29] [paper23]
[2018 w.48] [18-11-22] [paper22]
  • 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
[2018 w.47] [18-11-22] [paper21]
  • 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
[2018 w.46] [18-11-15] [paper19]
  • 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
[2018 w.45] [18-11-08] [paper17]
[2018 w.44] [18-09-27] [paper7]
  • 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
[2018 w.43] [18-10-25] [paper15]
  • 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
[2018 w.41] [18-10-04] [paper9]
  • On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
  • Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
  • 2018-05-29, NeurIPS2018
[2018 w.39] [18-09-27] [paper6]
[2018 w.38] [18-09-20] [paper1]
  • 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

SysCon Monte Carlo Reading Group:

[2019 w.6 II]
  • The Continuous-Discrete Time Feedback Particle Filter [pdf]
  • Tao Yang, Henk A. P. Blom, Prashant G. Mehta
  • 2014, American Control Conference
[2019 w.6 I]
  • Feedback Particle Filter [pdf]
  • Tao Yang, Prashant G. Mehta, Sean P. Meyn
  • 2013, IEEE Transactions on Automatic Control
[2019 w.3]
  • Markov Chains for Exploring Posterior Distributions [pdf] [pdf with comments]
  • Luke Tierney
  • 1994-12, The Annals of Statistics
[2018 w.50 II]
  • Particle Gibbs with Ancestor Sampling [pdf]
  • Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
  • 2014-06-14, Journal of Machine Learning Research
[2018 w.50 I]
  • Particle Markov chain Monte Carlo methods [pdf]
  • Christophe Andrieu, Arnaud Doucet, Roman Holenstein
  • 2010, Journal of the Royal Statistical Society
[2018 w.48]
  • 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
[2018 w.46]
  • Rethinking the Effective Sample Size [pdf]
  • Víctor Elvira, Luca Martino, Christian P. Robert
  • `2018-09-11,

NeurIPS:

NeurIPS 2019:

[20-01-31] [paper84]
[20-01-24] [paper83]
[20-01-15] [paper79]
[20-01-08] [paper76]
[19-12-15] [paper70]
[19-12-14] [paper69]

NeurIPS 2018:

[19-03-04] [paper49]
[19-02-27] [paper47]
[19-02-17] [paper44]
[19-02-05] [paper38]
[19-01-17] [paper28]
[19-01-09] [paper27]
  • 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
[18-12-12] [paper26]
[18-11-29] [paper23]
[18-11-22] [paper22]
  • 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
[18-10-04] [paper9]
  • On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
  • Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
  • 2018-05-29, NeurIPS2018

NeurIPS 2017:

[20-01-10] [paper77]
[19-05-29] [paper54]
  • 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
[18-10-18] [paper13]
  • Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
  • Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
  • 2017-11-17, NeurIPS2017
[18-09-24] [paper2]

NeurIPS 2016:

[19-10-18] [paper63]
  • Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
  • Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
  • 2016-06-15, NeurIPS2016

NeurIPS 2015:

[19-02-12] [paper41]
[19-01-25] [paper33]

NeurIPS 2011:

[19-01-28] [paper37]

ICML:

ICML 2019:

[20-02-14] [paper87]
[19-11-22] [paper65]

ICML 2018:

[20-02-13] [paper86]
[19-02-07] [paper40]
[18-11-15] [paper19]
  • 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
[18-10-18] [paper12]
  • 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
[18-09-30] [paper8]
  • 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
[18-09-27] [paper7]
  • 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
[18-09-27] [paper6]

ICML 2017:

[18-12-05] [paper24]

ICML 2015:

[19-10-11] [paper62]
[19-02-06] [paper39]
  • 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
[19-01-27] [paper36]

ICML 2014:

[19-01-23] [paper30]

ICML 2011:

[19-01-23] [paper29]

ICLR:

ICLR 2020:

[20-02-21] [paper90]
[20-01-17] [paper81]
[19-12-22] [paper74]

ICLR 2019:

[19-10-04] [paper61]
[19-03-29] [paper52]
  • Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
  • Jacob Menick, Nal Kalchbrenner
  • 2018-12-04, ICLR2019
[19-01-25] [paper34]
[18-11-22] [paper21]
  • 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
[18-11-08] [paper17]
[18-10-25] [paper15]
  • 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

ICLR 2018:

[18-09-20] [paper1]
  • 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

ICLR 2017:

[19-03-15] [paper51]

ICLR 2014:

[19-03-11] [paper50]

CVPR:

CVPR 2019:

[19-07-10] [paper59]
[19-06-12] [paper57]
[19-06-05] [paper55]
  • 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

CVPR 2018:

[18-10-05] [paper11]
[18-10-04] [paper10]
[18-09-24] [paper3]

CVPR 2016:

[19-01-26] [paper35]
  • 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

ECCV:

ECCV 2018:

[19-06-12] [paper56]
[18-11-16] [paper20]
  • 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

AISTATS:

AISTATS 2019:

[19-02-25] [paper46]
  • 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, AISTATS 2019

AISTATS 2010:

[20-01-14] [paper78]

AAAI:

AAAI 2020:

[19-12-19] [paper71]

CDC:

CDC 2018:

[19-10-28] [paper64]

JMLR:

[20-01-16] [paper80]

Papers by Year:

2020:

[20-03-09] [paper92]
[20-02-28] [paper91]
[20-02-18] [paper89]

2019:

[20-02-21] [paper90]
[20-02-15] [paper88]
[20-02-14] [paper87]
[20-01-31] [paper84]
[20-01-24] [paper83]
[20-01-17] [paper81]
[20-01-15] [paper79]
[20-01-08] [paper76]
[20-01-06] [paper75]
[19-12-22] [paper74]
[19-12-20] [paper72]
[19-12-19] [paper71]
[19-12-15] [paper70]
[19-12-14] [paper69]
[19-11-29] [paper67]
[19-07-11] [paper60]
  • 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
[19-07-03] [paper58]
[19-06-05] [paper55]
  • 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
[19-03-01] [paper48]
[19-02-25] [paper46]
  • 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
[19-02-22] [paper45]
  • 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
[19-02-14] [paper43]
[19-02-13] [paper42]

2018:

[19-12-20] [paper73]
[19-11-26] [paper66]
[19-11-22] [paper65]
[19-10-28] [paper64]
[19-10-04] [paper61]
[19-07-10] [paper59]
[19-06-12] [paper57]
[19-06-12] [paper56]
[19-03-29] [paper52]
  • Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
  • Jacob Menick, Nal Kalchbrenner
  • 2018-12-04, ICLR2019
[19-03-04] [paper49]
[19-02-27] [paper47]
[19-02-05] [paper38]
[19-01-25] [paper34]
[19-01-24] [paper31]
[19-01-17] [paper28]
[19-01-09] [paper27]
  • 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
[18-12-12] [paper26]
[18-12-06] [paper25]
[18-11-29] [paper23]
[18-11-22] [paper22]
  • 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
[18-11-22] [paper21]
  • 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
[18-11-16] [paper20]
  • 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
[18-11-15] [paper19]
  • 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
[18-11-12] [paper18]
  • Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
  • Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
  • 2018-11-08
[18-11-08] [paper17]
[18-10-26] [paper16]
  • 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
[18-10-25] [paper15]
  • 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
[18-10-19] [paper14]
  • Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
  • Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
  • 2018-10-12, AISTATS2019 submission
[18-10-18] [paper12]
  • 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
[18-10-04] [paper10]
[18-10-04] [paper9]
  • On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
  • Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
  • 2018-05-29, NeurIPS2018
[18-09-30] [paper8]
  • 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
[18-09-27] [paper7]
  • 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
[18-09-27] [paper6]
[18-09-25] [paper5]
  • 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
[18-09-25] [paper4]
  • 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
[18-09-24] [paper3]
[18-09-20] [paper1]
  • 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

2017:

[20-02-13] [paper86]
[20-02-08] [paper85]
[20-01-10] [paper77]
[19-05-29] [paper54]
  • 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
[19-04-05] [paper53]
  • 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)
[19-02-17] [paper44]
[19-02-07] [paper40]
[18-12-05] [paper24]
[18-10-18] [paper13]
  • Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
  • Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
  • 2017-11-17, NeurIPS2017
[18-10-05] [paper11]
[18-09-24] [paper2]

2016:

[19-10-18] [paper63]
  • Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
  • Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
  • 2016-06-15, NeurIPS2016
[19-03-15] [paper51]
[19-01-26] [paper35]
  • 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
[19-01-24] [paper32]
  • Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
  • Changyou Chen
  • 2016-08-10

2015:

[19-10-11] [paper62]
[19-02-12] [paper41]
[19-02-06] [paper39]
  • 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
[19-01-27] [paper36]
[19-01-25] [paper33]

2014:

[19-03-11] [paper50]
[19-01-23] [paper30]

2011:

[19-01-28] [paper37]
[19-01-23] [paper29]

2010:

[20-01-20] [paper82]

2009:

[20-01-14] [paper78]

2006:

[19-12-13] [paper68]

2004:

[20-01-16] [paper80]

papers's People

Contributors

dengnaitian avatar fregu856 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.