James Martens

发表

Adding Gradient Noise Improves Learning for Very Deep Networks pdf

Quoc V. Le, Lukasz Kaiser, Ilya Sutskever, 2015, ArXiv.

Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure

Jasper Snoek, Roger B. Grosse, David Duvenaud, 2018, ICLR.

On the importance of initialization and momentum in deep learning

Geoffrey E. Hinton, Ilya Sutskever, George E. Dahl, 2013, ICML.

Optimizing Neural Networks with Kronecker-factored Approximate Curvature

Roger B. Grosse, James Martens, James Martens, 2015, ICML.

A Kronecker-factored approximate Fisher matrix for convolution layers

Roger B. Grosse, James Martens, James Martens, 2016, ICML.

On the Expressive Power of Restricted Boltzmann Machines

Toniann Pitassi, Richard S. Zemel, Arkadev Chattopadhyay, 2013, NIPS.

Parallelizable Sampling of Markov Random Fields

Ilya Sutskever, James Martens, 2010, AISTATS.

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

Guodong Zhang, Roger B. Grosse, Sushant Sachdeva, 2019, NeurIPS.

Normalization for probabilistic inference with neurons

Chris Eliasmith, James Martens, James Martens, 2011, Biological Cybernetics.

Adversarial Robustness through Local Linearization

Pushmeet Kohli, Sven Gowal, Krishnamurthy Dvijotham, 2019, NeurIPS.

Generating Text with Recurrent Neural Networks

Geoffrey E. Hinton, Ilya Sutskever, James Martens, 2011, ICML.

A neurologically plausible implementation of statistical inference applied to random dot motion

Chris Eliasmith, James Martens, 2007, BMC Neuroscience.

Learning Recurrent Neural Networks with Hessian-Free Optimization

Ilya Sutskever, James Martens, Ilya Sutskever, 2011, ICML.

New perspectives on the natural gradient method pdf

James Martens, James Martens, 2014, ArXiv.

Estimating the Hessian by Back-propagating Curvature

Ilya Sutskever, Kevin Swersky, James Martens, 2012, ICML.

Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks

Guodong Zhang, Roger Grosse, James Martens, 2019, NeurIPS.

Distributed Second-Order Optimization using Kronecker-Factored Approximations

Roger B. Grosse, Jimmy Ba, James Martens, 2016, ICLR.

Differentiable Game Mechanics

Thore Graepel, Karl Tuyls, Jakob N. Foerster, 2019, J. Mach. Learn. Res..

The Mechanics of n-Player Differentiable Games pdf

Thore Graepel, Karl Tuyls, Jakob N. Foerster, 2018, ICML.

Learning the Linear Dynamical System with ASOS

James Martens, James Martens, 2010, ICML.

Training Deep and Recurrent Networks with Hessian-Free Optimization

Ilya Sutskever, James Martens, Ilya Sutskever, 2012, Neural Networks: Tricks of the Trade.

On the Expressive Efficiency of Sum Product Networks pdf

James Martens, Venkatesh Medabalimi, James Martens, 2014, ArXiv.

On the Variance of Unbiased Online Recurrent Optimization pdf

James Martens, Tim Cooijmans, James Martens, 2019, ArXiv.

Deep learning via Hessian-free optimization

James Martens, James Martens, 2010, ICML.

New Insights and Perspectives on the Natural Gradient Method

James Martens, James Martens, 2014, J. Mach. Learn. Res..

Kronecker-factored Curvature Approximations for Recurrent Neural Networks

Jimmy Ba, James Martens, Matt Johnson, 2018, ICLR.

Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping pdf

Samuel S. Schoenholz, Valentin Dalibard, Guillaume Desjardins, 2021, ArXiv.

On the validity of kernel approximations for orthogonally-initialized neural networks pdf

James Martens, James Martens, 2021, ArXiv.

A biologically realistic model of statistical inference applied to random dot motion

Chris Eliasmith, James Martens, 2007 .