Shakir Mohamed
Shakir Mohamed
Staff Research Scientist, DeepMind
Verified email at google.com - Homepage
TitleCited byYear
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
DJ Rezende, S Mohamed, D Wierstra
The 31st International Conference on Machine Learning (ICML), 2014
18882014
Semi-supervised learning with deep generative models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in neural information processing systems, 3581-3589, 2014
10932014
Variational inference with normalizing flows
DJ Rezende, S Mohamed
arXiv preprint arXiv:1505.05770, 2015
6252015
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, ...
ICLR 2 (5), 6, 2017
4802017
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
1832016
Learning in implicit generative models
S Mohamed, B Lakshminarayanan
arXiv preprint arXiv:1610.03483, 2016
1492016
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
1472016
Missing data: A comparison of neural network and expectation maximization techniques
FV Nelwamondo, S Mohamed, T Marwala
Current Science, 1514-1521, 2007
1232007
Variational information maximisation for intrinsically motivated reinforcement learning
S Mohamed, DJ Rezende
Advances in neural information processing systems, 2125-2133, 2015
1162015
Variational approaches for auto-encoding generative adversarial networks
M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed
arXiv preprint arXiv:1706.04987, 2017
1122017
Bayesian and L1 Approaches to Sparse Unsupervised Learning
S Mohamed, K Heller, Z Ghahramani
International Conference on Machine Learning, 2012
972012
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning.
S Mohamed, KA Heller, Z Ghahramani
International Conference on Machine Learning, 2012
97*2012
Recurrent environment simulators
S Chiappa, S Racaniere, D Wierstra, S Mohamed
arXiv preprint arXiv:1704.02254, 2017
822017
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
812016
The cramer distance as a solution to biased wasserstein gradients
MG Bellemare, I Danihelka, W Dabney, S Mohamed, ...
arXiv preprint arXiv:1705.10743, 2017
802017
Bayesian exponential family PCA
S Mohamed, K Heller, Z Ghahramani
Neural Information Processing Systems, 2008
792008
Adaptive Hamiltonian and Riemann Manifold Monte Carlo
Z Wang, S Mohamed, N de Freitas
Technical Report, Tech. Rep 951, 13, 2013
772013
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
612017
Unsupervised predictive memory in a goal-directed agent
G Wayne, CC Hung, D Amos, M Mirza, A Ahuja, A Grabska-Barwinska, ...
arXiv preprint arXiv:1803.10760, 2018
442018
Large scale nonparametric bayesian inference: Data parallelisation in the indian buffet process
F Doshi-Velez, D Knowles, S Mohamed, Z Ghahramani
Proceedings of the Conference on Neural Information Processing Systems (NIPS), 2009
432009
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Articles 1–20