Dropout as a Bayesian Approximation: Appendix

6 June 2015

Papers citing "Dropout as a Bayesian Approximation: Appendix"

13 / 13 papers shown

Title
Weight Uncertainty in Neural Networks Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra UQCV BDL 185 1,887 0 20 May 2015
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs Y. Gal Richard Turner 51 78 0 09 Mar 2015
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data Y. Gal Yutian Chen Zoubin Ghahramani SyDa 57 41 0 07 Mar 2015
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Y. Gal Mark van der Wilk C. Rasmussen 70 150 0 06 Feb 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,927 1 21 Dec 2013
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 452 16,929 0 20 Dec 2013
Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra Martin Riedmiller 127 12,231 0 19 Dec 2013
Gaussian Processes for Big Data J. Hensman Nicolò Fusi Neil D. Lawrence GP 107 1,232 0 26 Sep 2013
Dropout Training as Adaptive Regularization Stefan Wager Sida I. Wang Percy Liang 129 599 0 04 Jul 2013
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 127 1,182 0 02 Nov 2012
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 453 7,663 0 03 Jul 2012
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 259 2,622 0 29 Jun 2012
Variational Bayesian Inference with Stochastic Search John Paisley David M. Blei Michael I. Jordan BDL 106 499 0 27 Jun 2012