Summaries of papers on machine learning, computer vision, autonomous robots etc.
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commented_pdfs > Nov 12, 2018
summaries Update Large-Scale Visual Active Learning with Deep Probabilistic Ens… Nov 13, 2018
README.md Update README.md Nov 12, 2018

README.md

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:

[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]
  • The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
  • Jonathan Frankle, Michael Carbin
  • 2018-03-09, ICLR2019 submission
[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
[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, NIPS2017
[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]
[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:

[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, NIPS2017
[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]



3D Object Detection:

[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



Medical Imaging:




SysCon DL Reading Group:

[18-11-08] [paper17]
  • The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
  • Jonathan Frankle, Michael Carbin
  • 2018-03-09, ICLR2019 submission
[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
[18-10-25] [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
[18-10-04] [paper9]
[18-09-27] [paper6]
[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