S. Mohamed

发表

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression

Mohammad Emtiyaz Khan, Kevin P. Murphy, Shakir Mohamed, 2012, NIPS.

Unsupervised Predictive Memory in a Goal-Directed Agent pdf

Joel Z. Leibo, Demis Hassabis, Koray Kavukcuoglu, 2018, ArXiv.

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Daan Wierstra, Shakir Mohamed, Danilo Jimenez Rezende, 2014, ICML.

Semi-supervised Learning with Deep Generative Models

Max Welling, Danilo Jimenez Rezende, Diederik P. Kingma, 2014, NIPS.

Variational Inference with Normalizing Flows

Shakir Mohamed, Danilo Jimenez Rezende, S. Mohamed, 2015, ICML.

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework

Christopher Burgess, Shakir Mohamed, Matthew Botvinick, 2016, ICLR 2016.

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

Shakir Mohamed, Danilo Jimenez Rezende, S. Mohamed, 2015, NIPS.

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models pdf

Daan Wierstra, Shakir Mohamed, Danilo Jimenez Rezende, 2014, ArXiv.

Unsupervised Learning of 3D Structure from Images

Max Jaderberg, Nicolas Heess, Shakir Mohamed, 2016, NIPS.

Recurrent Environment Simulators pdf

Daan Wierstra, Shakir Mohamed, Silvia Chiappa, 2017, ICLR.

One-Shot Generalization in Deep Generative Models

Daan Wierstra, Karol Gregor, Ivo Danihelka, 2016, ICML.

Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process

Zoubin Ghahramani, Finale Doshi-Velez, David A. Knowles, 2009, NIPS.

A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury

Suman V. Ravuri, Jack W. Rae, Ivan V. Protsyuk, 2019, Nature.

The Cramer Distance as a Solution to Biased Wasserstein Gradients pdf

Marc G. Bellemare, Rémi Munos, Stephan Hoyer, 2017, ArXiv.

Implicit Reparameterization Gradients

Shakir Mohamed, Andriy Mnih, Mikhail Figurnov, 2018, NeurIPS.

Learning in Implicit Generative Models pdf

Shakir Mohamed, Balaji Lakshminarayanan, S. Mohamed, 2016, ArXiv.

Adaptive Hamiltonian and Riemann Manifold Monte Carlo

Nando de Freitas, Ziyu Wang, Shakir Mohamed, 2013, ICML.

Monte Carlo Gradient Estimation in Machine Learning pdf

Michael Figurnov, Shakir Mohamed, Andriy Mnih, 2019, J. Mach. Learn. Res..

Stochastic Gradient Estimation With Finite Differences

T. Weber, Lars Buesing, S. Mohamed, 2016 .

Normalizing Flows for Probabilistic Modeling and Inference pdf

Eric Nalisnick, Eric T. Nalisnick, Balaji Lakshminarayanan, 2019, J. Mach. Learn. Res..

Early Visual Concept Learning with Unsupervised Deep Learning pdf

Charles Blundell, Benigno Uria, Shakir Mohamed, 2016, ArXiv.

A Statistical View of Deep Learning

S. Mohamed, 2015 .

An Adaptive Strategy for the Classification of G-Protein Coupled Receptors pdf

Tshilidzi Marwala, Shakir Mohamed, David M. Rubin, 2007, ArXiv.

Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning

Katherine A. Heller, Zoubin Ghahramani, Shakir Mohamed, 2011, ICML.

N ov 2 01 6 Learning in Implicit Generative Models

S. Mohamed, Balaji Lakshminarayanan, 2016 .

Statistical Normalization and Back Propagation for Classification

S. Mohamed, M. Frize, C. M. Ennett, 2022 .

Dynamic protein classification: Adaptive models based on incremental learning strategies

S. Mohamed, 2008 .

Partial Membership and Factor Analysis

Katherine A. Heller, Zoubin Ghahramani, Shakir Mohamed, 2014, Handbook of Mixed Membership Models and Their Applications.

Bayesian and L$_\mathbf{1}$ Approaches to Sparse Unsupervised Learning

Katherine A. Heller, Zoubin Ghahramani, Shakir Mohamed, 2011 .

Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation

M. Deisenroth, S. Mohamed, Sanket Kamthe, 2022, Trans. Mach. Learn. Res..

Missing data: A comparison of neural network and expectation maximization techniques

S. Mohamed, T. Marwala, F. Nelwamondo, 2007, 0704.3474.

Gradient Penalties for Generative Adversarial Networks

Ian Goodfellow, Andrew M. Dai, Ian J. Goodfellow, 2018 .

GraphCast: Learning skillful medium-range global weather forecasting

Suman V. Ravuri, Oriol Vinyals, Timo Ewalds, 2022, ArXiv.

Generalised Bayesian matrix factorisation models

Shakir Mohamed, S. Mohamed, 2011 .

Skilful precipitation nowcasting using deep generative models of radar

Raia Hadsell, Karen Simonyan, Dmitry Kangin, 2021, Nature.

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Suman V. Ravuri, Jack W. Rae, Ivan V. Protsyuk, 2021, Nature Protocols.

Levels of Analysis for Machine Learning pdf

Jessica B. Hamrick, Jessica Hamrick, Shakir Mohamed, 2020, ArXiv.

A review of radar-based nowcasting of precipitation and applicable machine learning techniques pdf

Dmitry Kangin, Shakir Mohamed, Suman V. Ravuri, 2020, ArXiv.

Bayesian Exponential Family PCA

Katherine A. Heller, Zoubin Ghahramani, Shakir Mohamed, 2008, NIPS.

Learning Implicit Generative Models with the Method of Learned Moments pdf

Oriol Vinyals, Shakir Mohamed, Suman V. Ravuri, 2018, ICML.

Normalizing Flows on Riemannian Manifolds pdf

Shakir Mohamed, Danilo Jimenez Rezende, Mevlana Gemici, 2016, ArXiv.

Adaptive Hamiltonian and Riemann manifold Monte Carlo samplers

Nando de Freitas, Shakir Mohamed, ziyu wang, 2013, ICML 2013.

Incremental Learning for Classification of Protein Sequences

Tshilidzi Marwala, Shakir Mohamed, David M. Rubin, 2007, 2007 International Joint Conference on Neural Networks.

An Extension Neural Network and Genetic Algorithm for Bearing Fault Classification

Tshilidzi Marwala, Shakir Mohamed, Thando Tettey, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Machine learning and health need better values

S. Mohamed, M. Ghassemi, 2022, npj Digital Medicine.

Variational Approaches for Auto-Encoding Generative Adversarial Networks pdf

Shakir Mohamed, David Warde-Farley, Balaji Lakshminarayanan, 2017, ArXiv.

Training language GANs from Scratch

Shakir Mohamed, Jack W. Rae, Mihaela Rosca, 2019, NeurIPS.

Probabilistic non-negative tensor factorization using Markov chain Monte Carlo

Mikkel N. Schmidt, Shakir Mohamed, S. Mohamed, 2009, 2009 17th European Signal Processing Conference.

A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models

Mohammad Emtiyaz Khan, Kevin P. Murphy, Benjamin M. Marlin, 2012, AISTATS.

Bayesian and L1 Approaches to Sparse Unsupervised Learning pdf

Katherine A. Heller, Zoubin Ghahramani, Shakir Mohamed, 2011, ICML 2012.

Generative Temporal Models with Memory pdf

David Amos, Greg Wayne, Chia-Chun Hung, 2017, ArXiv.

Distribution Matching in Variational Inference pdf

Shakir Mohamed, Balaji Lakshminarayanan, Mihaela Rosca, 2018, ArXiv.

Interpretability Constraints and Trade-offs in Using Mixed Membership Models

S. Fienberg, E. Erosheva, Zoubin Ghahramani, 2014 .

Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step pdf

Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow, 2017, ICLR.

Understanding Deep Generative Models with Generalized Empirical Likelihoods

Suman V. Ravuri, M. Deisenroth, S. Mohamed, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

A case for new neural network smoothness constraints

T. Weber, S. Mohamed, A. Gretton, 2020, ICBINB@NeurIPS.

Neural Network Based Techniques for Estimating Missing Data in Databases

Tshilidzi Marwala, Shakir Mohamed, S. Mohamed, 2005 .

Expectation Propagation in Gaussian Process Dynamical Systems

Marc Peter Deisenroth, Shakir Mohamed, M. Deisenroth, 2012, NIPS.

Automatic Target Classification in SAR Images by Multilayer Back Propagation Neural Network

P. Vasuki, Mansoor Roomi, S. Mohamed, 2012 .