Shakir Mohamed

According to our database1, Shakir Mohamed authored at least 42 papers between 2006 and 2018.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2018
Learning Implicit Generative Models with the Method of Learned Moments.
CoRR, 2018

Implicit Reparameterization Gradients.
CoRR, 2018

Unsupervised Predictive Memory in a Goal-Directed Agent.
CoRR, 2018

Distribution Matching in Variational Inference.
CoRR, 2018

Learning Implicit Generative Models with the Method of Learned Moments.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step.
CoRR, 2017

Variational Approaches for Auto-Encoding Generative Adversarial Networks.
CoRR, 2017

Generative Temporal Models with Memory.
CoRR, 2017

Recurrent Environment Simulators.
CoRR, 2017

The Cramer Distance as a Solution to Biased Wasserstein Gradients.
CoRR, 2017

2016
One-Shot Generalization in Deep Generative Models.
CoRR, 2016

Unsupervised Learning of 3D Structure from Images.
CoRR, 2016

Learning in Implicit Generative Models.
CoRR, 2016

Early Visual Concept Learning with Unsupervised Deep Learning.
CoRR, 2016

Normalizing Flows on Riemannian Manifolds.
CoRR, 2016

Unsupervised Learning of 3D Structure from Images.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

One-Shot Generalization in Deep Generative Models.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Variational Inference with Normalizing Flows.
CoRR, 2015

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.
CoRR, 2015

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Variational Inference with Normalizing Flows.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Partial Membership and Factor Analysis.
Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models.
CoRR, 2014

Semi-Supervised Learning with Deep Generative Models.
CoRR, 2014

Semi-supervised Learning with Deep Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Stochastic Backpropagation and Approximate Inference in Deep Generative Models.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Adaptive Hamiltonian and Riemann Manifold Monte Carlo.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Expectation Propagation in Gaussian Process Dynamical Systems
CoRR, 2012

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Expectation Propagation in Gaussian Process Dynamical Systems.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning .
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Generalised Bayesian matrix factorisation models.
PhD thesis, 2011

Bayesian and L1 Approaches to Sparse Unsupervised Learning
CoRR, 2011

2009
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Probabilistic non-negative tensor factorization using Markov chain Monte Carlo.
Proceedings of the 17th European Signal Processing Conference, 2009

2008
Bayesian Exponential Family PCA.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007
An Adaptive Strategy for the Classification of G-Protein Coupled Receptors
CoRR, 2007

Incremental Learning for Classification of Protein Sequences.
Proceedings of the International Joint Conference on Neural Networks, 2007

2006
Multi-class Protein Sequence Classification Using Fuzzy ARTMAP.
Proceedings of the IEEE International Conference on Systems, 2006

An Extension Neural Network and Genetic Algorithm for Bearing Fault Classification.
Proceedings of the International Joint Conference on Neural Networks, 2006


  Loading...