Shakir Mohamed
Shakir Mohamed
Senior Staff Scientist, DeepMind
Verified email at - Homepage
Cited by
Cited by
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
DJ Rezende, S Mohamed, D Wierstra
The 31st International Conference on Machine Learning (ICML), 2014
Semi-supervised learning with deep generative models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in neural information processing systems, 3581-3589, 2014
beta-vae: Learning basic visual concepts with a constrained variational framework
I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ...
Variational inference with normalizing flows
DJ Rezende, S Mohamed
arXiv preprint arXiv:1505.05770, 2015
Unsupervised learning of 3d structure from images
DJ Rezende, SMA Eslami, S Mohamed, P Battaglia, M Jaderberg, ...
Advances in neural information processing systems, 4996-5004, 2016
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, DJ Rezende
Advances in neural information processing systems, 2125-2133, 2015
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
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
Variational approaches for auto-encoding generative adversarial networks
M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed
arXiv preprint arXiv:1706.04987, 2017
The cramer distance as a solution to biased wasserstein gradients
MG Bellemare, I Danihelka, W Dabney, S Mohamed, ...
arXiv preprint arXiv:1705.10743, 2017
Missing data: A comparison of neural network and expectation maximization techniques
FV Nelwamondo, S Mohamed, T Marwala
Current Science, 1514-1521, 2007
Recurrent environment simulators
S Chiappa, S Racaniere, D Wierstra, S Mohamed
arXiv preprint arXiv:1704.02254, 2017
Early visual concept learning with unsupervised deep learning
I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ...
arXiv preprint arXiv:1606.05579, 2016
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
Normalizing flows for probabilistic modeling and inference
G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ...
arXiv preprint arXiv:1912.02762, 2019
Bayesian and L1 Approaches to Sparse Unsupervised Learning
S Mohamed, K Heller, Z Ghahramani
International Conference on Machine Learning, 2012
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning.
S Mohamed, KA Heller, Z Ghahramani
International Conference on Machine Learning, 2012
Adaptive Hamiltonian and Riemann Manifold Monte Carlo
Z Wang, S Mohamed, N de Freitas
Technical Report, Tech. Rep 951, 13, 2013
Bayesian exponential family PCA
S Mohamed, K Heller, Z Ghahramani
Neural Information Processing Systems, 2008
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