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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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Batch Normalization Biases Deep Residual Networks Towards Shallow Paths

Feb 24, 2020
Soham De, Samuel L. Smith

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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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The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent

Apr 15, 2019
Karthik A. Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein

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Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration

Oct 08, 2018
Soham De, Anirbit Mukherjee, Enayat Ullah

* Presented on 14th July 2018 at the ICML Workshop on Modern Trends in Nonconvex Optimization for Machine Learning 

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Training Quantized Nets: A Deeper Understanding

Nov 13, 2017
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein

* NIPS 2017 

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Variance Reduction for Distributed Stochastic Gradient Descent

Apr 07, 2017
Soham De, Gavin Taylor, Tom Goldstein

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Efficient Distributed SGD with Variance Reduction

Apr 07, 2017
Soham De, Tom Goldstein

* In Proceedings of 2016 IEEE International Conference on Data Mining (ICDM) 

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Big Batch SGD: Automated Inference using Adaptive Batch Sizes

Apr 06, 2017
Soham De, Abhay Yadav, David Jacobs, Tom Goldstein

* A preliminary version of this paper appears in AISTATS 2017 (International Conference on Artificial Intelligence and Statistics) 

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Son of Zorn's Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation

Jan 09, 2017
Carlos Castillo, Soham De, Xintong Han, Bharat Singh, Abhay Kumar Yadav, Tom Goldstein

* ICASSP 2017 

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An Empirical Study of ADMM for Nonconvex Problems

Dec 10, 2016
Zheng Xu, Soham De, Mario Figueiredo, Christoph Studer, Tom Goldstein

* NIPS nonconvex workshop 

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Layer-Specific Adaptive Learning Rates for Deep Networks

Oct 15, 2015
Bharat Singh, Soham De, Yangmuzi Zhang, Thomas Goldstein, Gavin Taylor

* ICMLA 2015, deep learning, adaptive learning rates for training, layer specific learning rate 

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Plagiarism Detection in Polyphonic Music using Monaural Signal Separation

Feb 27, 2015
Soham De, Indradyumna Roy, Tarunima Prabhakar, Kriti Suneja, Sourish Chaudhuri, Rita Singh, Bhiksha Raj

* INTERSPEECH-2012, 1744-1747 (2012) 
* Preprint version 

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