| High-Performance Large-Scale Image Recognition Without Normalization A Brock, S De, SL Smith, K Simonyan International Conference on Machine Learning, 2021 | 415* | 2021 |
| 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, 13847-13856, 2019 | 279 | 2019 |
| Training quantized nets: A deeper understanding H Li*, S De*, Z Xu, C Studer, H Samet, T Goldstein Advances in Neural Information Processing Systems, 5813-5823, 2017 | 214 | 2017 |
| On the Origin of Implicit Regularization in Stochastic Gradient Descent SL Smith, B Dherin, DGT Barrett, S De International Conference on Learning Representations, 2021 | 141 | 2021 |
| Automated inference with adaptive batches S De, A Yadav, D Jacobs, T Goldstein Artificial Intelligence and Statistics, 1504-1513, 2017 | 136* | 2017 |
| The loosening of American culture over 200 years is associated with a creativity–order trade-off JC Jackson, M Gelfand, S De, A Fox Nature human behaviour 3 (3), 244-250, 2019 | 131 | 2019 |
| Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration S De, A Mukherjee, E Ullah ICML Workshop on Modern Trends in Nonconvex Optimization for Machine Learning, 2018 | 130* | 2018 |
| Batch normalization biases residual blocks towards the identity function in deep networks S De, S Smith Advances in Neural Information Processing Systems 33, 2020 | 124* | 2020 |
| The impact of neural network overparameterization on gradient confusion and stochastic gradient descent KA Sankararaman*, S De*, Z Xu, WR Huang, T Goldstein International Conference on Machine Learning, 8469-8479, 2020 | 96* | 2020 |
| BYOL works even without batch statistics PH Richemond, JB Grill, F Altché, C Tallec, F Strub, A Brock, S Smith, ... NeurIPS Workshop on Self-Supervised Learning: Theory and Practice, 2020 | 93* | 2020 |
| Characterizing signal propagation to close the performance gap in unnormalized ResNets A Brock, S De, SL Smith International Conference on Learning Representations, 2021 | 88 | 2021 |
| On the Generalization Benefit of Noise in Stochastic Gradient Descent S Smith, E Elsen, S De International Conference on Machine Learning, 9058-9067, 2020 | 84* | 2020 |
| Unlocking High-Accuracy Differentially Private Image Classification through Scale S De, L Berrada, J Hayes, SL Smith, B Balle ICML Workshop on Theory and Practice of Differential Privacy, 2022 | 82 | 2022 |
| Understanding norm change: An evolutionary game-theoretic approach S De, DS Nau, MJ Gelfand Proceedings of the 16th Conference on Autonomous Agents and MultiAgent …, 2017 | 63* | 2017 |
| Efficient distributed SGD with variance reduction S De, T Goldstein 2016 IEEE International Conference on Data Mining (ICDM), 2016 | 55* | 2016 |
| Layer-specific adaptive learning rates for deep networks B Singh, S De, Y Zhang, T Goldstein, G Taylor 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 54 | 2015 |
| Efficient neural network verification with exactness characterization KD Dvijotham, R Stanforth, S Gowal, C Qin, S De, P Kohli Uncertainty in Artificial Intelligence, 497-507, 2019 | 44 | 2019 |
| The Inevitability of Ethnocentrism Revisited: Ethnocentrism Diminishes As Mobility Increases S De, MJ Gelfand, D Nau, P Roos Scientific reports 5, 2015 | 44 | 2015 |
| An Empirical Study of ADMM for Nonconvex Problems Z Xu, S De, M Figueiredo, C Studer, T Goldstein NIPS 2016 Workshop on Nonconvex Optimization for Machine Learning: Theory …, 2016 | 39 | 2016 |
| Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error S Fort, A Brock, R Pascanu, S De, SL Smith arXiv preprint arXiv:2105.13343, 2021 | 21 | 2021 |
Samuel L. SmithDeepMindVerified email at google.com
