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James Martens
James Martens
Research Scientist, DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
On the importance of initialization and momentum in deep learning
I Sutskever, J Martens, G Dahl, G Hinton
International conference on machine learning, 1139-1147, 2013
62802013
Generating text with recurrent neural networks
I Sutskever, J Martens, GE Hinton
Proceedings of the 28th international conference on machine learning (ICML …, 2011
20512011
Deep learning via hessian-free optimization.
J Martens
Icml 27, 735-742, 2010
12662010
Optimizing neural networks with kronecker-factored approximate curvature
J Martens, R Grosse
International conference on machine learning, 2408-2417, 2015
10272015
Learning recurrent neural networks with hessian-free optimization
J Martens, I Sutskever
Proceedings of the 28th international conference on machine learning (ICML …, 2011
8182011
New insights and perspectives on the natural gradient method
J Martens
Journal of Machine Learning Research 21 (146), 1-76, 2020
6402020
Adding gradient noise improves learning for very deep networks
A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens
arXiv preprint arXiv:1511.06807, 2015
5922015
Adversarial robustness through local linearization
C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ...
Advances in neural information processing systems 32, 2019
3212019
The mechanics of n-player differentiable games
D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, T Graepel
International Conference on Machine Learning, 354-363, 2018
3162018
A kronecker-factored approximate fisher matrix for convolution layers
R Grosse, J Martens
International Conference on Machine Learning, 573-582, 2016
2742016
Training deep and recurrent networks with hessian-free optimization
J Martens, I Sutskever
Neural Networks: Tricks of the Trade: Second Edition, 479-535, 2012
2552012
Which algorithmic choices matter at which batch sizes? insights from a noisy quadratic model
G Zhang, L Li, Z Nado, J Martens, S Sachdeva, G Dahl, C Shallue, ...
Advances in neural information processing systems 32, 2019
1392019
Fast convergence of natural gradient descent for over-parameterized neural networks
G Zhang, J Martens, RB Grosse
Advances in Neural Information Processing Systems 32, 2019
1342019
Distributed Second-Order Optimization using Kronecker-Factored Approximations.
J Ba, RB Grosse, J Martens
ICLR (Poster), 2017
1062017
Differentiable game mechanics
A Letcher, D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, ...
Journal of Machine Learning Research 20 (84), 1-40, 2019
932019
Kronecker-factored curvature approximations for recurrent neural networks
J Martens, J Ba, M Johnson
International Conference on Learning Representations, 2018
912018
Pre-training via denoising for molecular property prediction
S Zaidi, M Schaarschmidt, J Martens, H Kim, YW Teh, ...
arXiv preprint arXiv:2206.00133, 2022
882022
On the representational efficiency of restricted boltzmann machines
J Martens, A Chattopadhya, T Pitassi, R Zemel
Advances in Neural Information Processing Systems 26, 2013
882013
Estimating the hessian by back-propagating curvature
J Martens, I Sutskever, K Swersky
arXiv preprint arXiv:1206.6464, 2012
882012
Second-order optimization for neural networks
J Martens
University of Toronto (Canada), 2016
732016
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