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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
66162013
Generating text with recurrent neural networks
I Sutskever, J Martens, GE Hinton
Proceedings of the 28th international conference on machine learning (ICML …, 2011
21362011
Deep learning via hessian-free optimization.
J Martens
Icml 27, 735-742, 2010
13122010
Optimizing neural networks with kronecker-factored approximate curvature
J Martens, R Grosse
International conference on machine learning, 2408-2417, 2015
11202015
Learning recurrent neural networks with hessian-free optimization
J Martens, I Sutskever
Proceedings of the 28th international conference on machine learning (ICML …, 2011
8362011
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ...
arXiv preprint arXiv:2403.05530, 2024
7242024
New insights and perspectives on the natural gradient method
J Martens
Journal of Machine Learning Research 21 (146), 1-76, 2020
7182020
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
6232015
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
3452019
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
3292018
A kronecker-factored approximate fisher matrix for convolution layers
R Grosse, J Martens
International Conference on Machine Learning, 573-582, 2016
2942016
Training deep and recurrent networks with hessian-free optimization
J Martens, I Sutskever
Neural Networks: Tricks of the Trade: Second Edition, 479-535, 2012
2682012
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
1512019
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
1492019
Distributed Second-Order Optimization using Kronecker-Factored Approximations.
J Ba, RB Grosse, J Martens
ICLR (Poster), 2017
1212017
Pre-training via denoising for molecular property prediction
S Zaidi, M Schaarschmidt, J Martens, H Kim, YW Teh, ...
arXiv preprint arXiv:2206.00133, 2022
1142022
Kronecker-factored curvature approximations for recurrent neural networks
J Martens, J Ba, M Johnson
International Conference on Learning Representations, 2018
1022018
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
992019
On the representational efficiency of restricted boltzmann machines
J Martens, A Chattopadhya, T Pitassi, R Zemel
Advances in Neural Information Processing Systems 26, 2013
892013
Estimating the hessian by back-propagating curvature
J Martens, I Sutskever, K Swersky
arXiv preprint arXiv:1206.6464, 2012
892012
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Articles 1–20