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Pre-training via Denoising for Molecular Property Prediction


May 31, 2022
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin

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Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers


Mar 15, 2022
Guodong Zhang, Aleksandar Botev, James Martens

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* ICLR 2022 

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


Oct 05, 2021
James Martens, Andy Ballard, Guillaume Desjardins, Grzegorz Swirszcz, Valentin Dalibard, Jascha Sohl-Dickstein, Samuel S. Schoenholz

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


Apr 13, 2021
James Martens

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


Jul 09, 2019
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger Grosse

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Adversarial Robustness through Local Linearization


Jul 04, 2019
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy, Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli

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Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks


May 27, 2019
Guodong Zhang, James Martens, Roger Grosse

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Differentiable Game Mechanics


May 13, 2019
Alistair Letcher, David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

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* Journal of Machine Learning Research (JMLR), v20 (84) 1-40, 2019 
* JMLR 2019, journal version of arXiv:1802.05642 

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On the Variance of Unbiased Online Recurrent Optimization


Feb 06, 2019
Tim Cooijmans, James Martens

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The Mechanics of n-Player Differentiable Games


Jun 06, 2018
David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

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* PMLR volume 80, 2018 
* ICML 2018, final version 

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