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Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications.
The Mathematical Theory of Communication
Claude E. Shannon and Warren Weaver · 1949
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A Mean Field Theory Learning Algorithm for Neural Networks
Carsten Peterson · 1987
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Elements of Information Theory
Thomas M. Cover and Joy A. Thomas · 1991
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A Practical Bayesian Framework for Backpropagation Networks
David JC MacKay · 1992
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Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights
Geoffrey E. Hinton and Drew van Camp · 1993
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Bayesian Learning for Neural Networks , volume 118
Radford M Neal · 1995
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Graphical Models, Exponential Families, and Variational Inference
Martin J Wainwright and Michael I Jordan · 2008
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Practical Variational Inference for Neural Networks
Alex Graves · 2011
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Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Stochastic Variational Inference
Matthew D. Hoffman, David M. Blei, Chong Wang, and John Paisley · 2013
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Current Management of Vitreous Hemorrhage due to Proliferative Diabetic Retinopathy
Jaafar El Annan and Petros E. Carvounis · 2014
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Dropout: A Simple Way to Prevent Neural Networks From Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Generating Sentences from a Continuous Space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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Diabetic Retinopathy Detection Dataset, 2015
EyePACS · 2015
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Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Concrete Problems in AI Safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Uncertainty in Deep Learning
Yarin Gal · 2016
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Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks
Michael Kampffmeyer, Arnt-Børre Salberg, and Robert Jenssen · 2016
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Modelling Uncertainty in Deep Learning for Camera Relocalization
Alex Kendall and Roberto Cipolla · 2016
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The Management of Grading Quality: Good Practice in the Quality Assurance of Grading
D. T. S. Widdowson · 2016
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Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks
Daniel E Worrall, Clare M Wilson, and Gabriel J Brostow · 2016
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Uncertainty-Aware Reinforcement Learning for Collision Avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks, 2019
Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, and Yarin Gal · 2019
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Deep Anomaly Detection with Outlier Exposure, 2019
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
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Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Andrey Malinin and Mark JF Gales · 2019
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Practical Deep Learning with Bayesian Principles
Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz E Khan, Anirudh Jain, Runa Eschenhagen, Richard E Turner, and Rio Yokota · 2019
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Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, and Ben Glocker · 2017
Cited alongside, same era.
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Leveraging Uncertainty Information From Deep Neural Networks for Disease Detection
Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl · 2017
Cited alongside, same era.
Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
Laurence Perreault Levasseur, Yashar D Hezaveh, and Risa H Wechsler · 2017
Cited alongside, same era.
Improving the Accuracy of Møller-Plesset Perturbation Theory with Neural Networks
Robert T McGibbon, Andrew G Taube, Alexander G Donchev, Karthik Siva, Felipe Hernández, Cory Hargus, Ka-Hei Law, John L Klepeis, and David E Shaw · 2017
Cited alongside, same era.
Opportunities and Obstacles for Deep Learning in Biology and Medicine
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman, et al · 2018
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Incidence and Progression of Diabetic Retinopathy: A Systematic Review
Charumathi Sabanayagam, Riswana Banu, Miao Li Chee, Ryan Lee, Ya Xing Wang, Gavin Tan, Jost B Jonas, Ecosse L Lamoureux, Ching-Yu Cheng, Barbara E K Klein, Paul Mitchell, Ronald Klein, C M Gemmy Cheung, and Tien Y Wong · 2019
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Aleatoric Uncertainty Estimation with Test-Time Augmentation for Medical Image Segmentation with Convolutional Neural Networks
Guotai Wang, Wenqi Li, Michael Aertsen, Jan Deprest, Sébastien Ourselin, and Tom Vercauteren · 2019
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Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E Turner, José Miguel Hernández-Lobato, and Alexander L Gaunt · 2019
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Experiment Tracking with Weights and Biases, 2020
Lukas Biewald · 2020
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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
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Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
Sebastian Farquhar, Michael A. Osborne, and Yarin Gal · 2020
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Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigkas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, and Yarin Gal · 2020
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Benchmarking Uncertainty Estimation Methods for Deep Learning With Safety-Related Metrics, 2020
Maximilian Henne, Adrian Schwaiger, Karsten Roscher, and Gereon Weiss · 2020
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Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
Tanya Nair, Doina Precup, Douglas L Arnold, and Tal Arbel · 2020
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How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Di Feng, Ali Harakeh, Steven L. Waslander, and Klaus Dietmayer · 2021
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WILDS: A Benchmark of In-the-Wild Distribution Shifts, 2021
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
Andrey Malinin, Neil Band, Yarin Gal, Mark Gales, Alexander Ganshin, German Chesnokov, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Denis Roginskiy, Mariya Shmatova, Panagiotis Tigas, and Boris Yangel · 2021
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Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, and Dustin Tran · 2021
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Plex: Towards reliability using pretrained large model extensions
Dustin Tran, Jeremiah Zhe Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda E Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, E. Kelly Buchanan, Kevin Patrick Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, and Balaji Lakshminarayanan · 2022
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