← Authors

Shakir Mohamed

19 papers · 2008–2023 · 6 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (6) 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (15) πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ‘‘ Triple Crown πŸ† Keyword Champion πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (5) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (111) πŸ’Ž Century Club (19)

Conferences

NIPS (9) ICML (4) AISTATS (2) JMLR (2) CVPR (1) ICLR (1)

Top co-authors

Danilo Jimenez Rezende (5) Mihaela Rosca (3) Andriy Mnih (2) Suman Ravuri (2) Zoubin Ghahramani (2) Balaji Lakshminarayanan (2) Nando Freitas (1) Mikhail Figurnov (1) Mohammad Khan (1) S. M. Ali Eslami (1)

Keywords

variational inference (8) deep generative model (4) approximate inference (4) bayesian inference (4) gaussian processes (2) normalizing flow (2) generative adversarial network (2) markov chain monte carlo (2) probabilistic model (2) gaussian process (2) semi-supervised learning (1) principal component analysis (1) matrix factorization (1) reinforcement learning (1) deep reinforcement learning (1) probabilistic modeling (1) 3d reconstruction (1) 3d vision (1) image synthesis (1) text generation (1)

Papers

Understanding Deep Generative Models With Generalized Empirical Likelihoods CVPR 2023
Normalizing Flows for Probabilistic Modeling and Inference JMLR 2021
Monte Carlo Gradient Estimation in Machine Learning JMLR 2020
Training Language GANs from Scratch NIPS 2019
Learning Implicit Generative Models with the Method of Learned Moments ICML 2018
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step ICLR 2018
Implicit Reparameterization Gradients NIPS 2018
Unsupervised Learning of 3D Structure from Images NIPS 2016
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning NIPS 2015
Variational Inference with Normalizing Flows ICML 2015
Semi-supervised Learning with Deep Generative Models NIPS 2014
Stochastic Backpropagation and Approximate Inference in Deep Generative Models ICML 2014
Adaptive Hamiltonian and Riemann Manifold Monte Carlo ICML 2013
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models AISTATS 2012
Expectation Propagation in Gaussian Process Dynamical Systems NIPS 2012
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression NIPS 2012
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models AISTATS 2012
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process NIPS 2009
Bayesian Exponential Family PCA NIPS 2008