Optimizing Neural Networks with Kronecker-factored Approximate Curvature

19 March 2015

Papers citing "Optimizing Neural Networks with Kronecker-factored Approximate Curvature"

28 / 228 papers shown

Title
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning Peter F. Schulam S. Saria OOD 27 103 0 02 Jan 2019
Natural Option Critic Saket Tiwari Philip S. Thomas 17 22 0 04 Dec 2018
Deep Frank-Wolfe For Neural Network Optimization Leonard Berrada Andrew Zisserman M. P. Kumar ODL 11 40 0 19 Nov 2018
Multi-Agent Generative Adversarial Imitation Learning Jiaming Song Hongyu Ren Dorsa Sadigh Stefano Ermon GAN 27 215 0 26 Jul 2018
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches Jie Liu Yu Rong Martin Takáč Junzhou Huang ODL 29 7 0 14 Jul 2018
Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis Thomas George César Laurent Xavier Bouthillier Nicolas Ballas Pascal Vincent ODL 29 150 0 11 Jun 2018
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach Ryo Karakida S. Akaho S. Amari FedML 47 140 0 04 Jun 2018
Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training Boyu Chen Wenlian Lu Ernest Fokoue 21 1 0 22 May 2018
Measuring and regularizing networks in function space Ari S. Benjamin David Rolnick Konrad Paul Kording 21 137 0 21 May 2018
On the insufficiency of existing momentum schemes for Stochastic Optimization Rahul Kidambi Praneeth Netrapalli Prateek Jain Sham Kakade ODL 27 117 0 15 Mar 2018
A Walk with SGD Chen Xing Devansh Arpit Christos Tsirigotis Yoshua Bengio 27 118 0 24 Feb 2018
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes Erin Grant Chelsea Finn Sergey Levine Trevor Darrell Thomas L. Griffiths BDL 21 505 0 26 Jan 2018
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients Jiaming Song Yuhuai Wu 19 2 0 17 Jan 2018
Riemannian approach to batch normalization Minhyung Cho Jaehyung Lee 29 93 0 27 Sep 2017
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation Yuhuai Wu Elman Mansimov Shun Liao Roger C. Grosse Jimmy Ba OffRL 22 622 0 17 Aug 2017
Kronecker Recurrent Units C. Jose Moustapha Cissé F. Fleuret ODL 24 45 0 29 May 2017
Online Natural Gradient as a Kalman Filter Yann Ollivier 25 67 0 01 Mar 2017
Relative Natural Gradient for Learning Large Complex Models Ke Sun Frank Nielsen 29 5 0 20 Jun 2016
On the Expressive Power of Deep Neural Networks M. Raghu Ben Poole Jon M. Kleinberg Surya Ganguli Jascha Narain Sohl-Dickstein 27 777 0 16 Jun 2016
Learning to learn by gradient descent by gradient descent Marcin Andrychowicz Misha Denil Sergio Gomez Colmenarejo Matthew W. Hoffman David Pfau Tom Schaul Brendan Shillingford Nando de Freitas 26 1,991 0 14 Jun 2016
Composing graphical models with neural networks for structured representations and fast inference Matthew J. Johnson David Duvenaud Alexander B. Wiltschko S. R. Datta Ryan P. Adams BDL OCL 22 482 0 20 Mar 2016
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans Diederik P. Kingma ODL 96 1,924 0 25 Feb 2016
Learning values across many orders of magnitude H. V. Hasselt A. Guez Matteo Hessel Volodymyr Mnih David Silver 17 169 0 24 Feb 2016
Improved Dropout for Shallow and Deep Learning Zhe Li Boqing Gong Tianbao Yang BDL SyDa 30 79 0 06 Feb 2016
A Kronecker-factored approximate Fisher matrix for convolution layers Roger C. Grosse James Martens ODL 34 258 0 03 Feb 2016
Adding Gradient Noise Improves Learning for Very Deep Networks Arvind Neelakantan Luke Vilnis Quoc V. Le Ilya Sutskever Lukasz Kaiser Karol Kurach James Martens AI4CE ODL 27 541 0 21 Nov 2015
Path-SGD: Path-Normalized Optimization in Deep Neural Networks Behnam Neyshabur Ruslan Salakhutdinov Nathan Srebro ODL 21 305 0 08 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 7,638 0 03 Jul 2012