Shakir Mohamed
Shakir Mohamed
Research Director, Google DeepMind
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
DJ Rezende, S Mohamed, D Wierstra
The 31st International Conference on Machine Learning (ICML), 2014
beta-vae: Learning basic visual concepts with a constrained variational framework.
I Higgins, L Matthey, A Pal, CP Burgess, X Glorot, MM Botvinick, ...
ICLR (Poster) 3, 2017
Variational inference with normalizing flows
D Rezende, S Mohamed
International conference on machine learning, 1530-1538, 2015
Semi-supervised learning with deep generative models
DP Kingma, S Mohamed, D Jimenez Rezende, M Welling
Advances in neural information processing systems 27, 2014
Normalizing flows for probabilistic modeling and inference
G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ...
Journal of Machine Learning Research 22 (57), 1-64, 2021
A clinically applicable approach to continuous prediction of future acute kidney injury
N Tomašev, X Glorot, JW Rae, M Zielinski, H Askham, A Saraiva, ...
Nature 572 (7767), 116-119, 2019
Skilful precipitation nowcasting using deep generative models of radar
S Ravuri, K Lenc, M Willson, D Kangin, R Lam, P Mirowski, M Fitzsimons, ...
Nature 597 (7878), 672-677, 2021
Learning in implicit generative models
S Mohamed, B Lakshminarayanan
arXiv preprint arXiv:1610.03483, 2016
Variational information maximisation for intrinsically motivated reinforcement learning
S Mohamed, D Jimenez Rezende
Advances in neural information processing systems 28, 2015
Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence
S Mohamed, MT Png, W Isaac
Philosophy & Technology 33, 659-684, 2020
Unsupervised learning of 3d structure from images
D Jimenez Rezende, SM Eslami, S Mohamed, P Battaglia, M Jaderberg, ...
Advances in neural information processing systems 29, 2016
Monte carlo gradient estimation in machine learning
S Mohamed, M Rosca, M Figurnov, A Mnih
Journal of Machine Learning Research 21 (132), 1-62, 2020
The cramer distance as a solution to biased wasserstein gradients
MG Bellemare, I Danihelka, W Dabney, S Mohamed, ...
arXiv preprint arXiv:1705.10743, 2017
Variational approaches for auto-encoding generative adversarial networks
M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed
arXiv preprint arXiv:1706.04987, 2017
One-shot generalization in deep generative models
D Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra
International Conference on Machine Learning, 1521-1529, 2016
Implicit reparameterization gradients
M Figurnov, S Mohamed, A Mnih
Advances in neural information processing systems 31, 2018
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
Missing data: A comparison of neural network and expectation maximization techniques
FV Nelwamondo, S Mohamed, T Marwala
Current Science, 1514-1521, 2007
AI for social good: unlocking the opportunity for positive impact
N Tomašev, J Cornebise, F Hutter, S Mohamed, A Picciariello, B Connelly, ...
Nature Communications 11 (1), 2468, 2020
Learning skillful medium-range global weather forecasting
R Lam, A Sanchez-Gonzalez, M Willson, P Wirnsberger, M Fortunato, ...
Science 382 (6677), 1416-1421, 2023
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