| 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 | 363 | 2020 |
| Variational approaches for auto-encoding generative adversarial networks M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed arXiv preprint arXiv:1706.04987, 2017 | 314 | 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 | 242 | 2017 |
| Deep compressed sensing Y Wu, M Rosca, T Lillicrap International Conference on Machine Learning, 6850-6860, 2019 | 164 | 2019 |
| Distribution matching in variational inference M Rosca, B Lakshminarayanan, S Mohamed arXiv preprint arXiv:1802.06847, 2018 | 99 | 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 | 75 | 2019 |
| Sequence-to-sequence neural network models for transliteration M Rosca, T Breuel arXiv preprint arXiv:1610.09565, 2016 | 61 | 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 | 47 | 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 | 36 | 2021 |
| A case for new neural network smoothness constraints M Rosca, T Weber, A Gretton, S Mohamed PMLR, 2020 | 31 | 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 | 30 | 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 | 28 | 2010 |
| Optax: composable gradient transformation and optimisation, in jax M Hessel, D Budden, F Viola, M Rosca, E Sezener, T Hennigan Github. http://github. com/google/jax, 2020 | 15 | 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 | 14 | 2022 |
| Discretization drift in two-player games MC Rosca, Y Wu, B Dherin, D Barrett International Conference on Machine Learning, 9064-9074, 2021 | 10 | 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 |
| 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 | 2 | 2023 |
| Compressed sensing using neural networks Y Wu, TP Lillicrap, M Rosca US Patent App. 16/818,895, 2020 | 2 | 2020 |
| Networks with emotions: An investigation into deep belief nets and emotion recognition M Rosca Imperial College London, 2014 | 2* | 2014 |
| Implicit regularisation in stochastic gradient descent: from single-objective to two-player games M Rosca, MP Deisenroth arXiv preprint arXiv:2307.05789, 2023 | 1 | 2023 |
Shakir MohamedResearch Director, Google DeepMindVerified email at deepmind.com
