Mohamed, Shakir

24 publications

CVPR 2023 Understanding Deep Generative Models with Generalized Empirical Likelihoods Suman Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth

TMLR 2022 Iterative State Estimation in Non-Linear Dynamical Systems Using Approximate Expectation Propagation Sanket Kamthe, So Takao, Shakir Mohamed, Marc Peter Deisenroth

JMLR 2021 Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

NeurIPSW 2020 A Case for New Neural Networks Smoothness Constraints Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed

JMLR 2020 Monte Carlo Gradient Estimation in Machine Learning Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

NeurIPS 2019 Training Language GANs from Scratch Cyprien de Masson d'Autume, Shakir Mohamed, Mihaela Rosca, Jack Rae

NeurIPS 2018 Implicit Reparameterization Gradients Mikhail Figurnov, Shakir Mohamed, Andriy Mnih

ICML 2018 Learning Implicit Generative Models with the Method of Learned Moments Suman Ravuri, Shakir Mohamed, Mihaela Rosca, Oriol Vinyals

ICLR 2018 Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence at Every Step William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

ICLR 2017 Beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner

ICLR 2017 Recurrent Environment Simulators Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed

NeurIPS 2016 Unsupervised Learning of 3D Structure from Images Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess

ICML 2015 Variational Inference with Normalizing Flows Danilo Rezende, Shakir Mohamed

NeurIPS 2015 Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning Shakir Mohamed, Danilo Jimenez Rezende

NeurIPS 2014 Semi-Supervised Learning with Deep Generative Models Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling

ICML 2014 Stochastic Backpropagation and Approximate Inference in Deep Generative Models Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

ICML 2013 Adaptive Hamiltonian and Riemann Manifold Monte Carlo Ziyu Wang, Shakir Mohamed, Nando Freitas

AISTATS 2012 A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models Mohammad Khan, Shakir Mohamed, Benjamin Marlin, Kevin Murphy

ICML 2012 Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani

NeurIPS 2012 Expectation Propagation in Gaussian Process Dynamical Systems Marc Deisenroth, Shakir Mohamed

NeurIPS 2012 Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression Mohammad Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy

AISTATS 2012 On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando De Freitas

NeurIPS 2009 Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process Finale Doshi-velez, Shakir Mohamed, Zoubin Ghahramani, David A. Knowles

NeurIPS 2008 Bayesian Exponential Family PCA Shakir Mohamed, Zoubin Ghahramani, Katherine A. Heller