Structured Dropout Variational Inference for Bayesian Neural Networks

16 February 2021

Papers citing "Structured Dropout Variational Inference for Bayesian Neural Networks"

24 / 24 papers shown

Title
Improving Multi-task Learning via Seeking Task-based Flat Regions Hoang Phan Lam C. Tran Ngoc N. Tran Nhat Ho Tuan Truong Qi Lei Nhat Ho Dinh Q. Phung Trung Le 168 11 0 24 Nov 2022
Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning Pan Zhou Jiashi Feng Chao Ma Caiming Xiong Guosheng Lin E. Weinan 71 234 0 12 Oct 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 53 213 0 14 May 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks J. Swiatkowski Kevin Roth Bastiaan S. Veeling Linh-Tam Tran Joshua V. Dillon Jasper Snoek Stephan Mandt Tim Salimans Rodolphe Jenatton Sebastian Nowozin BDL 41 45 0 07 Feb 2020
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 82 806 0 07 Feb 2019
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient Aaron Mishkin Frederik Kunstner Didrik Nielsen Mark Schmidt Mohammad Emtiyaz Khan BDL UQCV 56 60 0 11 Nov 2018
Variational Bayesian dropout: pitfalls and fixes Jiri Hron A. G. Matthews Zoubin Ghahramani BDL 60 67 0 05 Jul 2018
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Wu Lin Y. Gal Akash Srivastava ODL 116 270 0 13 Jun 2018
Semi-Implicit Variational Inference Mingzhang Yin Mingyuan Zhou BDL 69 128 0 28 May 2018
Sylvester Normalizing Flows for Variational Inference Rianne van den Berg Leonard Hasenclever Jakub M. Tomczak Max Welling BDL DRL 58 252 0 15 Mar 2018
Improving Generalization Performance by Switching from Adam to SGD N. Keskar R. Socher ODL 77 523 0 20 Dec 2017
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur Srinadh Bhojanapalli Nathan Srebro 77 605 0 29 Jul 2017
Spectrally-normalized margin bounds for neural networks Peter L. Bartlett Dylan J. Foster Matus Telgarsky ODL 175 1,216 0 26 Jun 2017
A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization Vineet Gupta Tomer Koren Y. Singer 27 22 0 20 Jun 2017
Bayesian Compression for Deep Learning Christos Louizos Karen Ullrich Max Welling UQCV BDL 137 479 0 24 May 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 56 1,028 0 23 May 2017
Variational Dropout Sparsifies Deep Neural Networks Dmitry Molchanov Arsenii Ashukha Dmitry Vetrov BDL 113 827 0 19 Jan 2017
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 196 1,510 0 08 Jun 2015
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 252 749 0 06 Jun 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 98 944 0 18 Feb 2015
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 408 16,947 0 20 Dec 2013
A PAC-Bayesian Tutorial with A Dropout Bound David A. McAllester 77 140 0 08 Jul 2013
Dropout Training as Adaptive Regularization Stefan Wager Sida I. Wang Percy Liang 120 599 0 04 Jul 2013
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 418 7,658 0 03 Jul 2012