| Monte carlo gradient estimation in machine learning S Mohamed, M Rosca, M Figurnov, A Mnih The Journal of Machine Learning Research 21 (1), 5183-5244, 2020 | 300 | 2020 |
| Variational approaches for auto-encoding generative adversarial networks M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed arXiv preprint arXiv:1706.04987, 2017 | 296 | 2017 |
| Many paths to equilibrium: GANs do not need to decrease a divergence at every step W Fedus, M Rosca, B Lakshminarayanan, AM Dai, S Mohamed, ... arXiv preprint arXiv:1710.08446, 2017 | 220 | 2017 |
| Deep compressed sensing Y Wu, M Rosca, T Lillicrap International Conference on Machine Learning, 6850-6860, 2019 | 149 | 2019 |
| Distribution matching in variational inference M Rosca, B Lakshminarayanan, S Mohamed arXiv preprint arXiv:1802.06847, 2018 | 92 | 2018 |
| Training language gans from scratch C de Masson d'Autume, S Mohamed, M Rosca, J Rae Advances in Neural Information Processing Systems 32, 2019 | 64 | 2019 |
| Sequence-to-sequence neural network models for transliteration M Rosca, T Breuel arXiv preprint arXiv:1610.09565, 2016 | 57 | 2016 |
| Optax: composable gradient transformation and optimisation M Hessel, D Budden, F Viola, M Rosca, E Sezener, T Hennigan JAX, http://github. com/deepmind/optax, 2020 | 39 | 2020 |
| Learning implicit generative models with the method of learned moments S Ravuri, S Mohamed, M Rosca, O Vinyals International Conference on Machine Learning, 4314-4323, 2018 | 29 | 2018 |
| Optax: composable gradient transformation and optimisation, in jax!, 2020 M Hessel, D Budden, F Viola, M Rosca, E Sezener, T Hennigan URL http://github. com/deepmind/optax 16, 2010 | 26 | 2010 |
| A case for new neural network smoothness constraints M Rosca, T Weber, A Gretton, S Mohamed PMLR, 2020 | 25 | 2020 |
| Spectral normalisation for deep reinforcement learning: an optimisation perspective F Gogianu, T Berariu, MC Rosca, C Clopath, L Busoniu, R Pascanu International Conference on Machine Learning, 3734-3744, 2021 | 24 | 2021 |
| Optax: composable gradient transformation and optimisation, in jax M Hessel, D Budden, F Viola, M Rosca, E Sezener, T Hennigan Github: Deepmind 2, 980-1080, 2020 | 12 | 2020 |
| Why neural networks find simple solutions: the many regularizers of geometric complexity B Dherin, M Munn, M Rosca, D Barrett Advances in Neural Information Processing Systems 35, 2333-2349, 2022 | 6 | 2022 |
| Discretization drift in two-player games MC Rosca, Y Wu, B Dherin, D Barrett International Conference on Machine Learning, 9064-9074, 2021 | 6 | 2021 |
| Measure-valued derivatives for approximate Bayesian inference M Rosca, M Figurnov, S Mohamed, A Mnih NeurIPS Workshop on Approximate Bayesian Inference, 2019 | 4 | 2019 |
| Networks with emotions: An investigation into deep belief nets and emotion recognition M Rosca Imperial College London, 2014 | 2* | 2014 |
| Compressed sensing using neural networks Y Wu, TP Lillicrap, M Rosca US Patent App. 16/818,895, 2020 | 1 | 2020 |
| Attractor neural networks for modelling associative memory W Al Jishi, N Hambuechen, R Marinescu, M Rosca, L Severyn January, 2013 | 1 | 2013 |
| On a continuous time model of gradient descent dynamics and instability in deep learning M Rosca, Y Wu, C Qin, B Dherin arXiv preprint arXiv:2302.01952, 2023 | | 2023 |
Shakir MohamedSenior Staff Scientist, DeepMindVerified email at deepmind.com
