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2020 – today
- 2024
[i18]Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney:
Disentangling the Causes of Plasticity Loss in Neural Networks. CoRR abs/2402.18762 (2024)- [i17]Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney:
Normalization and effective learning rates in reinforcement learning. CoRR abs/2407.01800 (2024) - 2023
- [c21]Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh:
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. ICLR 2023 - [c20]Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin:
Pre-training via Denoising for Molecular Property Prediction. ICLR 2023 - [i16]Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh:
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. CoRR abs/2302.10322 (2023) - 2022
- [c19]Guodong Zhang, Aleksandar Botev, James Martens:
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers. ICLR 2022 - [i15]Guodong Zhang, Aleksandar Botev, James Martens:
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers. CoRR abs/2203.08120 (2022) - [i14]Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin:
Pre-training via Denoising for Molecular Property Prediction. CoRR abs/2206.00133 (2022) - 2021
- [i13]James Martens:
On the validity of kernel approximations for orthogonally-initialized neural networks. CoRR abs/2104.05878 (2021) - [i12]James Martens, Andy Ballard, Guillaume Desjardins, Grzegorz Swirszcz, Valentin Dalibard, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping. CoRR abs/2110.01765 (2021) - 2020
- [j3]James Martens:
New Insights and Perspectives on the Natural Gradient Method. J. Mach. Learn. Res. 21: 146:1-146:76 (2020) - [c18]Rahma Mukta, James Martens, Hye-Young Paik
, Qinghua Lu, Salil S. Kanhere:
Blockchain-based Verifiable Credential Sharing with Selective Disclosure. TrustCom 2020: 959-966
2010 – 2019
- 2019
- [j2]Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
Differentiable Game Mechanics. J. Mach. Learn. Res. 20: 84:1-84:40 (2019) - [c17]Guodong Zhang, James Martens, Roger B. Grosse:
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks. NeurIPS 2019: 8080-8091 - [c16]Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. NeurIPS 2019: 8194-8205 - [c15]Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli:
Adversarial Robustness through Local Linearization. NeurIPS 2019: 13824-13833 - [i11]Tim Cooijmans, James Martens:
On the Variance of Unbiased Online Recurrent Optimization. CoRR abs/1902.02405 (2019) - [i10]Alistair Letcher, David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
Differentiable Game Mechanics. CoRR abs/1905.04926 (2019) - [i9]Guodong Zhang, James Martens, Roger B. Grosse:
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks. CoRR abs/1905.10961 (2019) - [i8]Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli:
Adversarial Robustness through Local Linearization. CoRR abs/1907.02610 (2019) - [i7]Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. CoRR abs/1907.04164 (2019) - 2018
- [c14]James Martens, Jimmy Ba, Matt Johnson:
Kronecker-factored Curvature Approximations for Recurrent Neural Networks. ICLR (Poster) 2018 - [c13]Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens:
Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. ICLR (Workshop) 2018 - [c12]David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
The Mechanics of n-Player Differentiable Games. ICML 2018: 363-372 - [i6]David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel:
The Mechanics of n-Player Differentiable Games. CoRR abs/1802.05642 (2018) - 2017
- [c11]Jimmy Ba, Roger B. Grosse, James Martens:
Distributed Second-Order Optimization using Kronecker-Factored Approximations. ICLR (Poster) 2017 - 2016
- [b1]James Martens:
Second-order Optimization for Neural Networks. University of Toronto, Canada, 2016 - [c10]Roger B. Grosse, James Martens:
A Kronecker-factored approximate Fisher matrix for convolution layers. ICML 2016: 573-582 - [i5]Roger B. Grosse, James Martens:
A Kronecker-factored approximate Fisher matrix for convolution layers. CoRR abs/1602.01407 (2016) - 2015
- [c9]James Martens, Roger B. Grosse:
Optimizing Neural Networks with Kronecker-factored Approximate Curvature. ICML 2015: 2408-2417 - [i4]James Martens, Roger B. Grosse:
Optimizing Neural Networks with Kronecker-factored Approximate Curvature. CoRR abs/1503.05671 (2015) - [i3]Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens:
Adding Gradient Noise Improves Learning for Very Deep Networks. CoRR abs/1511.06807 (2015) - 2014
- [i2]James Martens, Venkatesh Medabalimi:
On the Expressive Efficiency of Sum Product Networks. CoRR abs/1411.7717 (2014) - [i1]James Martens:
New perspectives on the natural gradient method. CoRR abs/1412.1193 (2014) - 2013
- [c8]Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton:
On the importance of initialization and momentum in deep learning. ICML (3) 2013: 1139-1147 - [c7]James Martens, Arkadev Chattopadhyay, Toniann Pitassi, Richard S. Zemel:
On the Expressive Power of Restricted Boltzmann Machines. NIPS 2013: 2877-2885 - 2012
- [c6]James Martens, Ilya Sutskever, Kevin Swersky:
Estimating the Hessian by Back-propagating Curvature. ICML 2012 - [p1]James Martens, Ilya Sutskever:
Training Deep and Recurrent Networks with Hessian-Free Optimization. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 479-535 - 2011
- [j1]Chris Eliasmith, James Martens:
Normalization for probabilistic inference with neurons. Biol. Cybern. 104(4-5): 251-262 (2011) - [c5]Ilya Sutskever, James Martens, Geoffrey E. Hinton:
Generating Text with Recurrent Neural Networks. ICML 2011: 1017-1024 - [c4]James Martens, Ilya Sutskever:
Learning Recurrent Neural Networks with Hessian-Free Optimization. ICML 2011: 1033-1040 - 2010
- [c3]James Martens:
Deep learning via Hessian-free optimization. ICML 2010: 735-742 - [c2]James Martens:
Learning the Linear Dynamical System with ASOS. ICML 2010: 743-750 - [c1]James Martens, Ilya Sutskever:
Parallelizable Sampling of Markov Random Fields. AISTATS 2010: 517-524
Coauthor Index
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last updated on 2024-09-13 01:38 CEST by the dblp team
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