About

Just to make it convenient for me and (possibly) for other folks I'll store some info about papers I've read. Moreover, I would like to use this repo as some kind of papers waiting-list, just to manage reading pipeline consistent.

Content

• Generative Models
• Reinforcement Learning
• Uncertainty Estimation
• Bayesian Methods
• Natural Language Processing
• Computer Vision

---

PhD Theses

[waiting][thesis5]

• Neural Density Estimation and Likelihood-free Inference
• George Papamakarios
• University of Edinburgh 2019
• [Uncertainty Estimation]

[waiting][thesis4]

• Uncertainty Detection in Natural Language Texts
• Veronika Vincze
• University of Szeged 2014
• [Natural Language Processing] [Uncertainty Estimation]

[waiting][thesis3]

• Bayesian Learning for Neural Networks
• Radford M. Neal
• University of Toronto 1995
• [Bayesian Methods] [Uncertainty Estimation]

[waiting][thesis2]

• Inference and Learning in Deep Generative Models [pdf]
• Casper Kaae Sønderby
• University of Copenhagen 2018
• [Generative Models]

[waiting][thesis1]

• Bayesian Methods for Adaptive Models [pdf]
• David Mackay
• Caltech 1992
• [Bayesian Methods] [Uncertatinty Estimation]

Computer Vision

[waiting][paper31]

• Image Transformer
• Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran
• ICML 2018
• [Computer Vision]

Federated Learning

[waiting][paper28]

• Federated Machine Learning: Concept and Applications
• Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong
• ACM TIST 2019
• [Federated Learning]

Generative Models

[waiting][paper41]

• Efficient GAN-Based Anomaly Detection
• Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar
• ICDM 2018
• [Uncertainty Estimation] [Generative Models]

[waiting][paper40]

• A Survey on GANs for Anomaly Detection
• Federico Di Mattia, Paolo Galeone, Michele De Simoni, Emanuele Ghelfi
• arxiv 2019
• [Uncertainty Estimation] [Generative Models]

[waiting][thesis2]

• Inference and Learning in Deep Generative Models [pdf]
• Casper Kaae Sønderby
• University of Copenhagen 2018
• [Generative Models]

Reinforcement Learning

[waiting][paper37]

• A Tutorial on Thompson Sampling
• Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen
• arxiv 2017
• [Uncertainty Estimation] [Reinforcement Learning]

[waiting][paper24]

• Exploration by Random Network Distillation [pdf]
• Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov
• ICLR 2019
• [Uncertainty Estimation] [Reinforcement Learning]

[waiting][paper9]

• Deep Exploration via Randomized Value Functions [pdf]
• Ian Osband, Benjamin Van Roy, Daniel Russo, Zheng Wen
• JMLR 2019
• [Reinforcement Learning] [Uncertatinty Estimation]

Uncertainty Estimation

[done][paper58]

• Normalizing Flows: An Introduction and Review of Current Methods
• Ivan Kobyzev, Simon J.D. Prince, Marcus A. Brubaker
• TPAMI 2020
• [Uncertainty Estimation]

[done][paper57]

• Uncertainty-Aware Deep Classifiers using Generative Models
• Murat Sensoy, Lance Kaplan, Federico Cerutti, Maryam Saleki
• AAAI 2020
• [Uncertainty Estimation]

[done][paper56]

• Deep Ensembles: A Loss Landscape Perspective
• Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
• arXiv 2019
• [Uncertainty Estimation]

[28-07-2020][paper54]

• Concrete Dropout
• Yarin Gal, Jiri Hron, Alex Kendall
• NIPS 2017
• [Uncertainty Estimation]

[28-07-2020][paper53]

• Uncertainty-guided Continual Learning with Bayesian Neural Networks
• Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach
• ICLR 2020
• [Uncertainty Estimation]

[27-07-2020][paper49]

• Epistemic Uncertainty Sampling
• Vu-Linh Nguyen, Sébastien Destercke, Eyke Hüllermeier
• ICDS 2019
• [Uncertainty Estimation]

[22-07-2020][paper48]

• Leveraging uncertainty information from deep neural networks for disease detection
• Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, Siegfried Wahl
• Nature 2017
• [Uncertainty Estimation]

[22-07-2020][paper47]

• Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
• Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel
• MICCAI 2018
• [Uncertainty Estimation]

[22-07-2020][paper46]

• Importance of being uncertain
• Martin Krzywinski, Naomi Altman
• Nature 2013
• [Uncertainty Estimation]

[waiting][paper44]

• Concrete Dropout
• Yarin Gal, Jiri Hron, Alex Kendall
• NIPS 2017
• [Bayesian Methods] [Uncertainty Estimation]

[waiting][paper43] [pdf]

• Uncertainty-guided Continual Learning with Bayesian Neural Networks
• Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach
• ICLR 2020
• [Bayesian Methods]

[waiting][paper41]

• Efficient GAN-Based Anomaly Detection
• Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar
• ICDM 2018
• [Uncertainty Estimation] [Generative Models]

[waiting][paper40]

• A Survey on GANs for Anomaly Detection
• Federico Di Mattia, Paolo Galeone, Michele De Simoni, Emanuele Ghelfi
• arxiv 2019
• [Uncertainty Estimation] [Generative Models]

[waiting][paper38]

• AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
• Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
• ICLR 2020
• [Uncertainty Estimation]

[waiting][paper37]

• A Tutorial on Thompson Sampling
• Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen
• arxiv 2017
• [Uncertainty Estimation] [Reinforcement Learning]

[waiting][paper36]

• Uncertainty in Structured Prediction
• Andrey Malinin, Mark Gales
• arxiv 2019
• [Uncertainty Estimation]

[waiting][paper35]

• Quantifying Uncertainties in Natural Language Processing Tasks
• Yijun Xiao, William Yang Wang
• AAAI 2019
• [Natural Language Processing] [Uncertainty Estimation]

[waiting][thesis5]

• Neural Density Estimation and Likelihood-free Inference
• George Papamakarios
• University of Edinburgh 2019
• [Uncertainty Estimation]

[waiting][thesis4]

• Uncertainty Detection in Natural Language Texts
• Veronika Vincze
• University of Szeged 2014
• [Natural Language Processing] [Uncertainty Estimation]

[waiting][paper27]

• Ensemble Distribution Distillation [pdf]
• Andrey Malinin, Bruno Mlodozeniec, Mark Gales
• ICLR 2020
• [Uncertainty Estimation]

[waiting][paper24]

• Exploration by Random Network Distillation [pdf]
• Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov
• ICLR 2019
• [Uncertainty Estimation] [Reinforcement Learning]

[waiting][paper23]

• Concrete Dropout [pdf]
• Yarin Gal, Jiri Hron, Alex Kendall
• NIPS 2017
• [Uncertainty Estimation] [Bayesian Methods]

[waiting][paper22]

• Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles [pdf]
• Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra
• NIPS 2016
• [Uncertainty Estimation]

[waiting][paper21]

• Accurate Uncertainty Estimation and Decomposition in Ensemble Learning [pdf]
• Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull
• NIPS 2019
• [Uncertainty Estimation]

[waiting][paper20]

• Direct Uncertainty Prediction for Medical Second Opinions [pdf]
• Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert Kleinberg, Sendhil Mullainathan, Jon Kleinberg
• ICML 2019
• [Uncertainty Estimation]

[waiting][paper19]

• Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
• Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
• CVPR 2020
• [Depth Estimation] [Uncertainty Estimation]

[waiting][paper16]

• On the uncertainty of self-supervised monocular depth estimation [[pdf]](On the uncertainty of self-supervised monocular depth estimation)
• Matteo Poggi Filippo Aleotti Fabio Tosi Stefano Mattoccia
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

[waiting][paper15]

• Scalable Uncertainty for Computer Vision with Functional Variational Inference [pdf]
• Eduardo D C Carvalho, Ronald Clark, Andrea Nicastro∗ Paul H J Kelly
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

[waiting][paper14]

• Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End [pdf]
• Abdelrahman Eldesokey, Michael Felsberg, Karl Holmquist, Michael Persson
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

[waiting][paper13]

• Deep Ensembles: A Loss Landscape Perspective
• Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
• arxiv 2019
• [Uncertatinty Estimation]

[waiting][paper12]

• A Simple Baseline for Bayesian Uncertainty in Deep Learning
• Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
• NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][thesis3]

• Bayesian Learning for Neural Networks
• Radford M. Neal
• University of Toronto 1995
• [Bayesian Methods] [Uncertainty Estimation]

[waiting][thesis1]

• Bayesian Methods for Adaptive Models [pdf]
• David Mackay
• Caltech 1992
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper11]

• Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf]
• Max Welling, Yee Whye Teh
• ICML 2011
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper10]

• Practical Deep Learning with Bayesian Principles [pdf]
• Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
• NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper9]

• Deep Exploration via Randomized Value Functions [pdf]
• Ian Osband, Benjamin Van Roy, Daniel Russo, Zheng Wen
• JMLR 2019
• [Reinforcement Learning] [Uncertatinty Estimation]

[waiting][paper8]

• On the Validity of Bayesian Neural Networks for Uncertainty Estimation [pds]
• John Mitros, Brian Mac Namee
• AICS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[24-06-2020][paper7]

• On Calibration of Modern Neural Networks
• Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
• ICLR 2017
• [Uncertatinty Estimation]

[waiting][paper6]

• BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
• Yeming Wen, Dustin Tran, Jimmy Ba
• ICLR 2020
• [Uncertatinty Estimation]

[10-06-2020][paper5]

• Weight Uncertainty in Neural Networks
• Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
• ICML 2015
• [Bayesian Methods] [Uncertatinty Estimation]

[10-06-2020][paper4]

• Bayesian Methods for Neural Networks [pdf]
• Christopher M. Bishop
• None
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper3]

• Neural Tangents: Fast and Easy Infinite Neural Networks in Python [pdf] [video]
• Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz
• 05-12-2019, ICML 2020
• [Bayesian Methods] [Uncertatinty Estimation] [Gaussian Processes]

[waiting][paper2]

• Bayesian Layers: A Module for Neural Network Uncertainty [pdf]
• Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
• 05-03-2019, NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

Bayesian Methods

[waiting][paper52]

• Practical Variational Inference for Neural Networks
• Alex Graves
• NIPS 2011
• [Bayesian Methods]

[waiting][paper45]

• Variational Inference: A Review for Statisticians
• David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
• JASA 2017
• [Bayesian Methods]

[waiting][paper44]

• Concrete Dropout
• Yarin Gal, Jiri Hron, Alex Kendall
• NIPS 2017
• [Bayesian Methods] [Uncertainty Estimation]

[waiting][paper43] [pdf]

• Uncertainty-guided Continual Learning with Bayesian Neural Networks
• Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach
• ICLR 2020
• [Bayesian Methods]

[waiting][paper42] [pdf]

• Explaining the Gibbs Sampler
• George Casella, Edward I. George
• TAS 1992
• [Bayesian Methods]

[waiting][paper34]

• Normalizing Flows for Probabilistic Modeling and Inference
• George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
• review article 2019
• [Bayesian Methods]

[waiting][paper23]

• Concrete Dropout [pdf]
• Yarin Gal, Jiri Hron, Alex Kendall
• NIPS 2017
• [Uncertainty Estimation] [Bayesian Methods]

[waiting][paper17]

• Multiplicative Normalizing Flows for Variational Bayesian Neural Networks [pdf]
• Christos Louizos, Max Welling
• ICML 2017
• [Bayesian Methods]

[waiting][paper12]

• A Simple Baseline for Bayesian Uncertainty in Deep Learning
• Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
• NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][thesis3]

• Bayesian Learning for Neural Networks
• Radford M. Neal
• University of Toronto 1995
• [Bayesian Methods] [Uncertainty Estimation]

[waiting][thesis1]

• Bayesian Methods for Adaptive Models [pdf]
• David Mackay
• Caltech 1992
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper11]

• Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf]
• Max Welling, Yee Whye Teh
• ICML 2011
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper10]

• Practical Deep Learning with Bayesian Principles [pdf]
• Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
• NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper8]

• On the Validity of Bayesian Neural Networks for Uncertainty Estimation [pdf]
• John Mitros, Brian Mac Namee
• AICS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper5]

• Weight Uncertainty in Neural Networks
• Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
• ICML 2015
• [Bayesian Methods] [Uncertatinty Estimation]

[10-06-2020][paper4]

• Bayesian Methods for Neural Networks [pdf]
• Christopher M. Bishop
• None
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper3]

• Neural Tangents: Fast and Easy Infinite Neural Networks in Python [pdf] [video]
• Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz
• 05-12-2019, ICML 2020
• [Bayesian Methods] [Uncertatinty Estimation] [Gaussian Processes]

[waiting][paper2]

• Bayesian Layers: A Module for Neural Network Uncertainty [pdf]
• Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
• 05-03-2019, NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

Natural Language Processing

[done][paper55]

• Improving language understanding by generative pre-training
• Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever
• ?
• [Natural Language Processing]

[done][paper54]

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
• ACL 2019
• [Natural Language Processing]

[waiting][paper35]

• Quantifying Uncertainties in Natural Language Processing Tasks
• Yijun Xiao, William Yang Wang
• AAAI 2019
• [Natural Language Processing] [Uncertainty Estimation]

[waiting][paper4]

• Uncertainty Detection in Natural Language Texts
• Veronika Vincze
• University of Szeged 2014
• [Natural Language Processing] [Uncertainty Estimation]

[waiting][paper32]

• Neural Machine Translation of Rare Words with Subword Units
• Rico Sennrich, Barry Haddow, Alexandra Birch
• ACL 2016
• [Natural Language Processing]

[waiting][paper30]

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
• ACL 2017
• [Natural Language Processing]

[waiting][paper29]

• Attention Is All You Need
• Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
• NIPS 2017
• [Natural Language Processing]

[waiting][paper1]

• Unsupervised Translation of Programming Languages [pdf]
• Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot, Guillaume Lample
• 05-06-2020, arxiv
• [Natural Language Processing]

Gaussian Processes

[waiting][paper18]

• Deep Gaussian Processes [pdf]
• Andreas C. Damianou, Neil D. Lawrence
• AISTATS 2013
• [Gaussian Processes]

[waiting][paper3]

• Neural Tangents: Fast and Easy Infinite Neural Networks in Python [pdf] [video]
• Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz
• 05-12-2019, ICML 2020
• [Bayesian Methods] [Uncertatinty Estimation] [Gaussian Processes]

Depth Estimation

[waiting][paper19]

• Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
• Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
• CVPR 2020
• [Depth Estimation] [Uncertainty Estimation]

[waiting][paper16]

• On the uncertainty of self-supervised monocular depth estimation [[pdf]](On the uncertainty of self-supervised monocular depth estimation)
• Matteo Poggi Filippo Aleotti Fabio Tosi Stefano Mattoccia
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

[waiting][paper15]

• Scalable Uncertainty for Computer Vision with Functional Variational Inference [pdf]
• Eduardo D C Carvalho, Ronald Clark, Andrea Nicastro∗ Paul H J Kelly
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

[waiting][paper14]

• Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End [pdf]
• Abdelrahman Eldesokey, Michael Felsberg, Karl Holmquist, Michael Persson
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

Active Learning

[22-07-2020][paper50]

• A Sequential Algorithm for Training Text Classifiers
• David D. Lewis, William A. Gale
• ICDS 2019
• [Uncertainty Estimation]

Random

Place for papers, that I don't really want to categorize ;)

[waiting][paper51]

• Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
• Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse
• ICLR 2018
• [Random]

[waiting][paper39]

• Pointer Networks
• Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
• NIPS 2015
• [Random]

[waiting][paper33]

• Layer Normalization
• Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
• ? 2016
• [Random]

2020

[done][paper58]

• Normalizing Flows: An Introduction and Review of Current Methods
• Ivan Kobyzev, Simon J.D. Prince, Marcus A. Brubaker
• TPAMI 2020
• [Uncertainty Estimation]

[done][paper57]

• Uncertainty-Aware Deep Classifiers using Generative Models
• Murat Sensoy, Lance Kaplan, Federico Cerutti, Maryam Saleki
• AAAI 2020
• [Uncertainty Estimation]

[done][paper56]

• Deep Ensembles: A Loss Landscape Perspective
• Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
• arXiv 2019
• [Uncertainty Estimation]

[done][paper55]

• Improving language understanding by generative pre-training
• Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever
• ?
• [Natural Language Processing]

[done][paper54]

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
• ACL 2019
• [Natural Language Processing]

[done][paper54]

• Concrete Dropout
• Yarin Gal, Jiri Hron, Alex Kendall
• NIPS 2017
• [Uncertainty Estimation]

[done][paper53]

• Uncertainty-guided Continual Learning with Bayesian Neural Networks
• Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach
• ICLR 2020
• [Uncertainty Estimation]

[waiting][paper52]

• Practical Variational Inference for Neural Networks
• Alex Graves
• NIPS 2011
• [Bayesian Methods]

[waiting][paper51]

• Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
• Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse
• ICLR 2018
• [Random]

[done][paper50]

• A Sequential Algorithm for Training Text Classifiers
• David D. Lewis, William A. Gale
• ICDS 2019
• [Uncertainty Estimation]

[22-07-2020][paper49]

• Epistemic Uncertainty Sampling
• Vu-Linh Nguyen, Sébastien Destercke, Eyke Hüllermeier
• ICDS 2019
• [Uncertainty Estimation]

[22-07-2020][paper48]

• Leveraging uncertainty information from deep neural networks for disease detection
• Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, Siegfried Wahl
• Nature 2017
• [Uncertainty Estimation]

[22-07-2020][paper47]

• Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
• Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel
• MICCAI 2018
• [Uncertainty Estimation]

[22-07-2020][paper46]

• Importance of being uncertain
• Martin Krzywinski, Naomi Altman
• Nature 2013
• [Uncertainty Estimation]

[waiting][paper45]

• Variational Inference: A Review for Statisticians
• David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
• JASA 2017
• [Bayesian Methods]

[waiting][paper44]

• Concrete Dropout
• Yarin Gal, Jiri Hron, Alex Kendall
• NIPS 2017
• [Bayesian Methods] [Uncertainty Estimation]

[waiting][paper43] [pdf]

• Uncertainty-guided Continual Learning with Bayesian Neural Networks
• Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach
• ICLR 2020
• [Bayesian Methods]

[waiting][paper42] [pdf]

• Explaining the Gibbs Sampler
• George Casella, Edward I. George
• TAS 1992
• [Bayesian Methods]

[waiting][paper41]

• Efficient GAN-Based Anomaly Detection
• Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar
• ICDM 2018
• [Uncertainty Estimation] [Generative Models]

[waiting][paper40]

• A Survey on GANs for Anomaly Detection
• Federico Di Mattia, Paolo Galeone, Michele De Simoni, Emanuele Ghelfi
• arxiv 2019
• [Uncertainty Estimation] [Generative Models]

[waiting][paper39]

• Pointer Networks
• Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
• NIPS 2015
• [Random]

[waiting][paper38]

• AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
• Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
• ICLR 2020
• [Uncertainty Estimation]

[waiting][paper37]

• A Tutorial on Thompson Sampling
• Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen
• arxiv 2017
• [Uncertainty Estimation] [Reinforcement Learning]

[waiting][paper36]

• Uncertainty in Structured Prediction
• Andrey Malinin, Mark Gales
• arxiv 2019
• [Uncertainty Estimation]

[waiting][paper35]

• Quantifying Uncertainties in Natural Language Processing Tasks
• Yijun Xiao, William Yang Wang
• AAAI 2019
• [Natural Language Processing] [Uncertainty Estimation]

[waiting][paper34]

• Normalizing Flows for Probabilistic Modeling and Inference
• George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
• review article 2019
• [Bayesian Methods]

[waiting][paper33]

• Layer Normalization
• Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
• ? 2016
• [Random]

[waiting][paper32]

• Neural Machine Translation of Rare Words with Subword Units
• Rico Sennrich, Barry Haddow, Alexandra Birch
• ACL 2016
• [Natural Language Processing]

[waiting][paper31]

• Image Transformer
• Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran
• ICML 2018
• [Computer Vision]

[waiting][paper30]

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
• ACL 2017
• [Natural Language Processing]

[waiting][paper29]

• Attention Is All You Need
• Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
• NIPS 2017
• [Natural Language Processing]

[waiting][paper28]

• Federated Machine Learning: Concept and Applications
• Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong
• ACM TIST 2019
• [Federated Learning]

[waiting][paper27]

• Ensemble Distribution Distillation [pdf]
• Andrey Malinin, Bruno Mlodozeniec, Mark Gales
• ICLR 2020
• [Uncertainty Estimation]

[waiting][paper26]

• Domain-Adaptive Multibranch Networks
• Bermúdez Chacón, Róger ; Salzmann, Mathieu ; Fua, Pascal
• ICLR 2020
• [[Domain Adaptation]]

[waiting][paper25]

• DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection [pdf]
• Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales and Javier Ortega-Garcia
• arxiv
• [[DeepFakes]]

[waiting][paper24]

• Exploration by Random Network Distillation [pdf]
• Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov
• ICLR 2019
• [Uncertainty Estimation] [Reinforcement Learning]

[waiting][paper23]

• Concrete Dropout [pdf]
• Yarin Gal, Jiri Hron, Alex Kendall
• NIPS 2017
• [Uncertainty Estimation] [Bayesian Methods]

[waiting][paper22]

• Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles [pdf]
• Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra
• NIPS 2016
• [Uncertainty Estimation]

[waiting][paper21]

• Accurate Uncertainty Estimation and Decomposition in Ensemble Learning [pdf]
• Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull
• NIPS 2019
• [Uncertainty Estimation]

[waiting][paper20]

• Direct Uncertainty Prediction for Medical Second Opinions [pdf]
• Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert Kleinberg, Sendhil Mullainathan, Jon Kleinberg
• ICML 2019
• [Uncertainty Estimation]

[waiting][paper19]

• Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
• Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
• CVPR 2020
• [Depth Estimation] [Uncertainty Estimation]

[waiting][paper18]

• Deep Gaussian Processes [pdf]
• Andreas C. Damianou, Neil D. Lawrence
• AISTATS 2013
• [Gaussian Processes]

[waiting][paper17]

• Multiplicative Normalizing Flows for Variational Bayesian Neural Networks [pdf]
• Christos Louizos, Max Welling
• ICML 2017
• [Bayesian Methods]

[waiting][paper16]

• On the uncertainty of self-supervised monocular depth estimation [[pdf]](On the uncertainty of self-supervised monocular depth estimation)
• Matteo Poggi Filippo Aleotti Fabio Tosi Stefano Mattoccia
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

[waiting][paper15]

• Scalable Uncertainty for Computer Vision with Functional Variational Inference [pdf]
• Eduardo D C Carvalho, Ronald Clark, Andrea Nicastro∗ Paul H J Kelly
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

[waiting][paper14]

• Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End [pdf]
• Abdelrahman Eldesokey, Michael Felsberg, Karl Holmquist, Michael Persson
• CVPR 2020
• [Uncertatinty Estimation] [Depth Estimation]

[waiting][paper13]

• Deep Ensembles: A Loss Landscape Perspective
• Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
• arxiv 2019
• [Uncertatinty Estimation]

[waiting][paper12]

• A Simple Baseline for Bayesian Uncertainty in Deep Learning
• Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
• NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper11]

• Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf]
• Max Welling, Yee Whye Teh
• ICML 2011
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper10]

• Practical Deep Learning with Bayesian Principles [pdf]
• Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
• NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper9]

• Deep Exploration via Randomized Value Functions [pdf]
• Ian Osband, Benjamin Van Roy, Daniel Russo, Zheng Wen
• JMLR 2019
• [Reinforcement Learning] [Uncertatinty Estimation]

[waiting][paper8]

• On the Validity of Bayesian Neural Networks for Uncertainty Estimation [pdf]
• John Mitros, Brian Mac Namee
• AICS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[24-06-2020][paper7]

• On Calibration of Modern Neural Networks
• Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
• ICLR 2017
• [Uncertatinty Estimation]

[14-06-2020][paper6]

• BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
• Yeming Wen, Dustin Tran, Jimmy Ba
• ICLR 2020
• [Uncertatinty Estimation]

[10-06-2020][paper5]

• Weight Uncertainty in Neural Networks
• Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
• ICML 2015
• [Bayesian Methods] [Uncertatinty Estimation]

[10-06-2020][paper4]

• Bayesian Methods for Neural Networks [pdf]
• Christopher M. Bishop
• None
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper3]

• Neural Tangents: Fast and Easy Infinite Neural Networks in Python [pdf] [video]
• Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz
• 05-12-2019, ICML 2020
• [Bayesian Methods] [Uncertatinty Estimation] [Gaussian Processes]

[waiting][paper2]

• Bayesian Layers: A Module for Neural Network Uncertainty [pdf]
• Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
• 05-03-2019, NIPS 2019
• [Bayesian Methods] [Uncertatinty Estimation]

[waiting][paper1]

• Unsupervised Translation of Programming Languages [pdf]
• Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot, Guillaume Lample
• 05-06-2020, arxiv
• [Natural Language Processing]