URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

8 July 2020

Papers citing "URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks"

29 / 29 papers shown

Title
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks Meet P. Vadera B. Jalaeian Benjamin M. Marlin BDL FedML UQCV 43 20 0 16 May 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors Michael W. Dusenberry Ghassen Jerfel Yeming Wen Yi-An Ma Jasper Snoek Katherine A. Heller Balaji Lakshminarayanan Dustin Tran UQCV BDL 47 209 0 14 May 2020
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification Meet P. Vadera Satya Narayan Shukla B. Jalaeian Benjamin M. Marlin AAML BDL 31 6 0 07 Feb 2020
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat Brian Karrer Daniel R. Jiang Sam Daulton Benjamin Letham A. Wilson E. Bakshy 49 93 0 14 Oct 2019
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo Adam D. Cobb A. G. Baydin Andrew Markham Stephen J. Roberts 40 32 0 14 Oct 2019
Subspace Inference for Bayesian Deep Learning Pavel Izmailov Wesley J. Maddox Polina Kirichenko T. Garipov Dmitry Vetrov A. Wilson UQCV BDL 58 144 0 17 Jul 2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia Emily Fertig Jie Jessie Ren Zachary Nado D. Sculley Sebastian Nowozin Joshua V. Dillon Balaji Lakshminarayanan Jasper Snoek UQCV 137 1,677 0 06 Jun 2019
Ensemble Distribution Distillation A. Malinin Bruno Mlodozeniec Mark Gales UQCV 54 232 0 30 Apr 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 105 3,399 0 28 Mar 2019
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning Ruqi Zhang Chunyuan Li Jianyi Zhang Changyou Chen A. Wilson BDL 59 275 0 11 Feb 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 74 801 0 07 Feb 2019
Deep Learning for Classical Japanese Literature Tarin Clanuwat Mikel Bober-Irizar A. Kitamoto Alex Lamb Kazuaki Yamamoto David R Ha 68 705 0 03 Dec 2018
Adversarial Distillation of Bayesian Neural Network Posteriors Kuan-Chieh Wang Paul Vicol James Lucas Li Gu Roger C. Grosse R. Zemel UQCV GAN AAML BDL 34 56 0 27 Jun 2018
Loss-Calibrated Approximate Inference in Bayesian Neural Networks Adam D. Cobb Stephen J. Roberts Y. Gal BDL UQCV 42 43 0 10 May 2018
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi-Velez Steffen Udluft UQCV PER BDL UD 46 27 0 19 Oct 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 157 8,807 0 25 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 225 11,962 0 19 Jun 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 193 5,774 0 14 Jun 2017
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 98 3,420 0 07 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 163 8,513 0 16 Aug 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 254 7,951 0 23 May 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.3K 192,638 0 10 Dec 2015
Bayesian Dark Knowledge Masashi Sugiyama Vivek Rathod R. Garnett Max Welling BDL UQCV 48 258 0 14 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 457 9,233 0 06 Jun 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 159 18,922 0 20 Dec 2014
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 69 902 0 17 Feb 2014
Expectation Propagation for approximate Bayesian inference T. Minka 101 1,906 0 10 Jan 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 191 2,605 0 29 Jun 2012
Elliptical slice sampling Iain Murray Ryan P. Adams D. MacKay 111 465 0 31 Dec 2009