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Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings

3 September 2020

Papers citing "Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings"

38 / 38 papers shown

Title
Robust Out-of-distribution Detection for Neural Networks Jiefeng Chen Yixuan Li Xi Wu Yingyu Liang S. Jha OODD 205 87 0 21 Mar 2020
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One Will Grathwohl Kuan-Chieh Wang J. Jacobsen David Duvenaud Mohammad Norouzi Kevin Swersky VLM 100 547 0 06 Dec 2019
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory Micah Goldblum Jonas Geiping Avi Schwarzschild Michael Moeller Tom Goldstein 88 34 0 01 Oct 2019
Verified Uncertainty Calibration Ananya Kumar Percy Liang Tengyu Ma 185 359 0 23 Sep 2019
On the Expressiveness of Approximate Inference in Bayesian Neural Networks Andrew Y. K. Foong David R. Burt Yingzhen Li Richard Turner UQCV BDL 70 20 0 02 Sep 2019
On Mutual Information Maximization for Representation Learning Michael Tschannen Josip Djolonga Paul Kishan Rubenstein Sylvain Gelly Mario Lucic SSL 184 502 0 31 Jul 2019
Understanding the Limitations of Conditional Generative Models Ethan Fetaya J. Jacobsen Will Grathwohl R. Zemel 84 54 0 04 Jun 2019
Enhancing Adversarial Defense by k-Winners-Take-All Chang Xiao Peilin Zhong Changxi Zheng AAML 78 99 0 25 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 196 3,458 0 28 Mar 2019
Identity Crisis: Memorization and Generalization under Extreme Overparameterization Chiyuan Zhang Samy Bengio Moritz Hardt Michael C. Mozer Y. Singer 51 90 0 13 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 171 2,052 0 08 Feb 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 109 809 0 07 Feb 2019
Deep Learning for Anomaly Detection: A Survey Raghavendra Chalapathy Sanjay Chawla AI4TS 172 1,495 0 10 Jan 2019
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning Peter F. Schulam Suchi Saria OOD 101 104 0 02 Jan 2019
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 185 1,487 0 11 Dec 2018
A Less Biased Evaluation of Out-of-distribution Sample Detectors Alireza Shafaei Mark Schmidt James J. Little OODD 118 58 0 13 Sep 2018
Open Category Detection with PAC Guarantees Si Liu Risheek Garrepalli Thomas G. Dietterich Alan Fern Dan Hendrycks 61 84 0 01 Aug 2018
Uncertainty in the Variational Information Bottleneck Alexander A. Alemi Ian S. Fischer Joshua V. Dillon 51 100 0 02 Jul 2018
Why do deep convolutional networks generalize so poorly to small image transformations? Aharon Azulay Yair Weiss 92 562 0 30 May 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 112 1,785 0 30 May 2018
Averaging Weights Leads to Wider Optima and Better Generalization Pavel Izmailov Dmitrii Podoprikhin T. Garipov Dmitry Vetrov A. Wilson FedML MoMe 147 1,673 0 14 Mar 2018
Predictive Uncertainty Estimation via Prior Networks A. Malinin Mark Gales UD BDL EDL UQCV PER 203 923 0 28 Feb 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Ajmal Mian AAML 105 1,871 0 02 Jan 2018
Deep and Confident Prediction for Time Series at Uber Lingxue Zhu N. Laptev BDL AI4TS 169 345 0 06 Sep 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 289 8,928 0 25 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 319 12,151 0 19 Jun 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,877 0 14 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 131 1,867 0 20 May 2017
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt Matthew D. Hoffman David M. Blei BDL 71 599 0 13 Apr 2017
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 179 3,482 0 07 Oct 2016
Towards Bayesian Deep Learning: A Framework and Some Existing Methods Hao Wang Dit-Yan Yeung BDL 62 225 0 24 Aug 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 362 8,005 0 23 May 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 309 4,817 0 04 Jan 2016
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop Feng Yu Ari Seff Yinda Zhang Shuran Song Thomas Funkhouser Jianxiong Xiao 119 2,346 0 10 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 894 9,364 0 06 Jun 2015
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 293 14,978 1 21 Dec 2013
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 151 1,184 0 02 Nov 2012
Approximate Bayesian Computational methods Jean-Michel Marin Pierre Pudlo Christian P. Robert Robin J. Ryder 258 867 0 05 Jan 2011