# James Martens

According to our database

Collaborative distances:

^{1}, James Martens authored at least 19 papers between 2010 and 2018.Collaborative distances:

## Timeline

#### Legend:

Book In proceedings Article PhD thesis Other## Links

#### On csauthors.net:

## Bibliography

2018

The Mechanics of n-Player Differentiable Games.

CoRR, 2018

The Mechanics of n-Player Differentiable Games.

Proceedings of the 35th International Conference on Machine Learning, 2018

2016

A Kronecker-factored approximate Fisher matrix for convolution layers.

CoRR, 2016

A Kronecker-factored approximate Fisher matrix for convolution layers.

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Adding Gradient Noise Improves Learning for Very Deep Networks.

CoRR, 2015

Optimizing Neural Networks with Kronecker-factored Approximate Curvature.

CoRR, 2015

Optimizing Neural Networks with Kronecker-factored Approximate Curvature.

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

On the Expressive Efficiency of Sum Product Networks.

CoRR, 2014

New perspectives on the natural gradient method.

CoRR, 2014

2013

On the Expressive Power of Restricted Boltzmann Machines.

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

On the importance of initialization and momentum in deep learning.

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Training Deep and Recurrent Networks with Hessian-Free Optimization.

Proceedings of the Neural Networks: Tricks of the Trade - Second Edition, 2012

Estimating the Hessian by Back-propagating Curvature.

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Normalization for probabilistic inference with neurons.

Biological Cybernetics, 2011

Generating Text with Recurrent Neural Networks.

Proceedings of the 28th International Conference on Machine Learning, 2011

Learning Recurrent Neural Networks with Hessian-Free Optimization.

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Parallelizable Sampling of Markov Random Fields.

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Learning the Linear Dynamical System with ASOS.

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Deep learning via Hessian-free optimization.

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010