| Don't Decay the Learning Rate, Increase the Batch Size SL Smith, PJ Kindermans, C Ying, QV Le International Conference on Learning Representations, 2018 | 1176 | 2018 |
| Ultrafast long-range charge separation in organic semiconductor photovoltaic diodes S Gélinas, A Rao, A Kumar, SL Smith, AW Chin, J Clark, TS van der Poll, ... Science 343 (6170), 512-516, 2014 | 1007 | 2014 |
| Offline bilingual word vectors, orthogonal transformations and the inverted softmax SL Smith, DHP Turban, S Hamblin, NY Hammerla International Conference on Learning Representations, 2017 | 598 | 2017 |
| High-performance large-scale image recognition without normalization A Brock, S De, SL Smith, K Simonyan International Conference on Machine Learning, 1059-1071, 2021 | 478 | 2021 |
| A Bayesian Perspective on Generalization and Stochastic Gradient Descent SL Smith, QV Le International Conference on Learning Representations, 2018 | 385 | 2018 |
| The future of quantum biology A Marais, B Adams, AK Ringsmuth, M Ferretti, JM Gruber, R Hendrikx, ... Journal of the Royal Society Interface 15 (148), 20180640, 2018 | 220 | 2018 |
| On the Origin of Implicit Regularization in Stochastic Gradient Descent SL Smith, B Dherin, DGT Barrett, S De arXiv preprint arXiv:2101.12176, 2021 | 174 | 2021 |
| Batch normalization biases residual blocks towards the identity function in deep networks S De, S Smith Advances in Neural Information Processing Systems 33, 19964-19975, 2020 | 147* | 2020 |
| Unlocking high-accuracy differentially private image classification through scale S De, L Berrada, J Hayes, SL Smith, B Balle arXiv preprint arXiv:2204.13650, 2022 | 135 | 2022 |
| Characterizing signal propagation to close the performance gap in unnormalized ResNets A Brock, S De, SL Smith arXiv preprint arXiv:2101.08692, 2021 | 117 | 2021 |
| Resurrecting recurrent neural networks for long sequences A Orvieto, SL Smith, A Gu, A Fernando, C Gulcehre, R Pascanu, S De International Conference on Machine Learning, 26670-26698, 2023 | 91 | 2023 |
| On the Generalization Benefit of Noise in Stochastic Gradient Descent S Smith, E Elsen, S De International Conference on Machine Learning, 9058-9067, 2020 | 91 | 2020 |
| BYOL works even without batch statistics PH Richemond, JB Grill, F Altché, C Tallec, F Strub, A Brock, S Smith, ... arXiv preprint arXiv:2010.10241, 2020 | 85 | 2020 |
| The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study DS Park, J Sohl-Dickstein, QV Le, SL Smith International Conference on Machine Learning, 2019 | 55 | 2019 |
| Phonon-assisted ultrafast charge separation in the PCBM band structure SL Smith, AW Chin Physical Review B 91 (20), 201302, 2015 | 41 | 2015 |
| Ultrafast charge separation and nongeminate electron–hole recombination in organic photovoltaics SL Smith, AW Chin Physical Chemistry Chemical Physics 16 (38), 20305-20309, 2014 | 41 | 2014 |
| Differentially Private Diffusion Models Generate Useful Synthetic Images S Ghalebikesabi, L Berrada, S Gowal, I Ktena, R Stanforth, J Hayes, S De, ... arXiv preprint arXiv:2302.13861, 2023 | 30 | 2023 |
| 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 | 25 | 2021 |
| Gemma: Open Models Based on Gemini Research and Technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 23 | 2024 |
| Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation B He, J Martens, G Zhang, A Botev, A Brock, SL Smith, YW Teh arXiv preprint arXiv:2302.10322, 2023 | 18 | 2023 |
Soham DeDeepMindVerified email at google.com
