Dropout Inference in Bayesian Neural Networks with Alpha-divergences

8 March 2017

Papers citing "Dropout Inference in Bayesian Neural Networks with Alpha-divergences"

36 / 36 papers shown

Title
Bayesian posterior approximation with stochastic ensembles Oleksandr Balabanov Bernhard Mehlig H. Linander BDL UQCV 27 5 0 15 Dec 2022
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics Kamélia Daudel Joe Benton Yuyang Shi Arnaud Doucet DRL 16 8 0 12 Oct 2022
On the use of uncertainty in classifying Aedes Albopictus mosquitoes Gereziher W. Adhane Mohammad Mahdi Dehshibi David Masip 23 7 0 29 Oct 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 38 1,109 0 07 Jul 2021
The Hidden Uncertainty in a Neural Networks Activations Janis Postels Hermann Blum Yannick Strümpler Cesar Cadena Roland Siegwart Luc Van Gool Federico Tombari UQCV 30 22 0 05 Dec 2020
Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images J. E. Arco A. Ortiz J. Ramírez Francisco J. Martínez-Murcia Yudong Zhang Juan M Gorriz UQCV 19 3 0 27 Nov 2020
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks Theodoros Tsiligkaridis UQCV 22 7 0 19 Oct 2020
SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation Sungmin Kang R. Feldt S. Yoo AAML 26 32 0 19 May 2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks Ginevra Carbone Matthew Wicker Luca Laurenti A. Patané Luca Bortolussi G. Sanguinetti AAML 38 77 0 11 Feb 2020
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study David Mickisch F. Assion Florens Greßner W. Günther M. Motta AAML 19 34 0 05 Feb 2020
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning Andreas Sedlmeier Thomas Gabor Thomy Phan Lenz Belzner Claudia Linnhoff-Popien 18 25 0 31 Dec 2019
Active Learning for Deep Detection Neural Networks H. H. Aghdam Abel Gonzalez-Garcia Joost van de Weijer Antonio M. López VLM ObjD 30 137 0 20 Nov 2019
An Adaptive Empirical Bayesian Method for Sparse Deep Learning Wei Deng Xiao Zhang F. Liang Guang Lin BDL 18 42 0 23 Oct 2019
Variational Inference MPC for Bayesian Model-based Reinforcement Learning Masashi Okada T. Taniguchi 32 73 0 08 Jul 2019
Survey of Dropout Methods for Deep Neural Networks Alex Labach Hojjat Salehinejad S. Valaee 27 149 0 25 Apr 2019
Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks K. Posch J. Pilz UQCV BDL 16 28 0 02 Apr 2019
Variational Inference to Measure Model Uncertainty in Deep Neural Networks K. Posch J. Steinbrener J. Pilz UQCV BDL 14 27 0 26 Feb 2019
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic Mikael Henaff A. Canziani Yann LeCun OOD 28 122 0 08 Jan 2019
Building robust classifiers through generation of confident out of distribution examples K. Sricharan Ashok Srivastava OOD 8 31 0 01 Dec 2018
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting Artur Bekasov Iain Murray AAML BDL 14 14 0 29 Nov 2018
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network Xuanqing Liu Yao Li Chongruo Wu Cho-Jui Hsieh AAML OOD 19 171 0 01 Oct 2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 21 397 0 21 Sep 2018
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 24 44 0 12 Jun 2018
Variational Implicit Processes Chao Ma Yingzhen Li José Miguel Hernández-Lobato BDL 22 68 0 06 Jun 2018
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 60 952 0 05 Jun 2018
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation Manuel Haussmann Fred Hamprecht M. Kandemir BDL 18 6 0 19 May 2018
Hierarchical Novelty Detection for Visual Object Recognition Kibok Lee Kimin Lee Kyle Min Y. Zhang Jinwoo Shin Honglak Lee BDL 44 67 0 02 Apr 2018
Understanding Measures of Uncertainty for Adversarial Example Detection Lewis Smith Y. Gal UQCV 43 358 0 22 Mar 2018
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks Mattias Teye Hossein Azizpour Kevin Smith BDL UQCV 25 239 0 18 Feb 2018
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples Kimin Lee Honglak Lee Kibok Lee Jinwoo Shin OODD 64 872 0 26 Nov 2017
Deep and Confident Prediction for Time Series at Uber Lingxue Zhu N. Laptev BDL AI4TS 22 342 0 06 Sep 2017
Towards Robust Detection of Adversarial Examples Tianyu Pang Chao Du Yinpeng Dong Jun Zhu AAML 23 18 0 02 Jun 2017
Concrete Dropout Y. Gal Jiri Hron Alex Kendall BDL UQCV 29 585 0 22 May 2017
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 197 745 0 06 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,636 0 03 Jul 2012