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Unlocking High-Accuracy Differentially Private Image Classification through Scale


Apr 28, 2022
Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, Borja Balle


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Regularising for invariance to data augmentation improves supervised learning


Mar 07, 2022
Aleksander Botev, Matthias Bauer, Soham De


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A study on the plasticity of neural networks


May 31, 2021
Tudor Berariu, Wojciech Czarnecki, Soham De, Jorg Bornschein, Samuel Smith, Razvan Pascanu, Claudia Clopath


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Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error


May 27, 2021
Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, Samuel L. Smith


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High-Performance Large-Scale Image Recognition Without Normalization


Feb 11, 2021
Andrew Brock, Soham De, Samuel L. Smith, Karen Simonyan


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On the Origin of Implicit Regularization in Stochastic Gradient Descent


Jan 28, 2021
Samuel L. Smith, Benoit Dherin, David G. T. Barrett, Soham De

* Accepted as a conference paper at ICLR 2021 

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Characterizing signal propagation to close the performance gap in unnormalized ResNets


Jan 27, 2021
Andrew Brock, Soham De, Samuel L. Smith

* Published as a conference paper at ICLR 2021 

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BYOL works even without batch statistics


Oct 20, 2020
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel Smith, Soham De, Razvan Pascanu, Bilal Piot, Michal Valko


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On the Generalization Benefit of Noise in Stochastic Gradient Descent


Jun 26, 2020
Samuel L. Smith, Erich Elsen, Soham De

* Camera-ready version of ICML 2020 

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