Shakir Mohamed

According to our database, Shakir Mohamed authored at least 55 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The illusion of artificial inclusion.

William Agnew, A. Stevie Bergman, Jennifer Chien, Mark Díaz, Seliem El-Sayed, Jaylen Pittman, Shakir Mohamed, Kevin R. McKee

CoRR, 2024

2023

GenCast: Diffusion-based ensemble forecasting for medium-range weather.

Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Timo Ewalds, Andrew El-Kadi, Jacklynn Stott, Shakir Mohamed, Peter W. Battaglia, Rémi Lam, Matthew Willson

CoRR, 2023

Understanding Deep Generative Models with Generalized Empirical Likelihoods.

Suman V. Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

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

Sanket Kamthe, So Takao, Shakir Mohamed, Marc Peter Deisenroth

Trans. Mach. Learn. Res., 2022

Machine learning and health need better values.

Marzyeh Ghassemi, Shakir Mohamed

npj Digit. Medicine, 2022

GraphCast: Learning skillful medium-range global weather forecasting.

Rémi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Pritzel, Suman V. Ravuri, Timo Ewalds, Ferran Alet, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Jacklynn Stott, Oriol Vinyals, Shakir Mohamed, Peter W. Battaglia

CoRR, 2022

se-Shweshwe Inspired Fashion Generation.

Lindiwe Brigitte Malobola, Negar Rostamzadeh, Shakir Mohamed

CoRR, 2022

Power to the People? Opportunities and Challenges for Participatory AI.

Abeba Birhane, William Isaac, Vinodkumar Prabhakaran, Mark Diaz, Madeleine Clare Elish, Iason Gabriel, Shakir Mohamed

Proceedings of the Equity and Access in Algorithms, Mechanisms, and Optimization, 2022

2021

Skilful precipitation nowcasting using deep generative models of radar.

Suman V. Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Rémi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, Rachel Prudden, Amol Mandhane, Aidan Clark, Andrew Brock, Karen Simonyan, Raia Hadsell, Niall H. Robinson, Ellen Clancy, Alberto Arribas, Shakir Mohamed

Nat., 2021

Normalizing Flows for Probabilistic Modeling and Inference.

George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan

J. Mach. Learn. Res., 2021

Skillful Precipitation Nowcasting using Deep Generative Models of Radar.

Suman V. Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Rémi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, Rachel Prudden, Amol Mandhane, Aidan Clark, Andrew Brock, Karen Simonyan, Raia Hadsell, Niall H. Robinson, Ellen Clancy, Alberto Arribas, Shakir Mohamed

CoRR, 2021

Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities.

Nenad Tomasev, Kevin R. McKee, Jackie Kay, Shakir Mohamed

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020

Domesticating the techno-racial project.

Shakir Mohamed

Nat. Mach. Intell., 2020

Monte Carlo Gradient Estimation in Machine Learning.

Shakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih

J. Mach. Learn. Res., 2020

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence.

Shakir Mohamed, Marie-Therese Png, William Isaac

CoRR, 2020

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

Rachel Prudden, Samantha V. Adams, Dmitry Kangin, Niall H. Robinson, Suman V. Ravuri, Shakir Mohamed, Alberto Arribas

CoRR, 2020

Levels of Analysis for Machine Learning.

Jessica B. Hamrick, Shakir Mohamed

CoRR, 2020

A case for new neural network smoothness constraints.

Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019

Training Language GANs from Scratch.

Cyprien de Masson d'Autume, Shakir Mohamed, Mihaela Rosca, Jack W. Rae

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Unsupervised Predictive Memory in a Goal-Directed Agent.

Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack W. Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Jimenez Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matthew M. Botvinick, Demis Hassabis, Timothy P. Lillicrap

CoRR, 2018

Distribution Matching in Variational Inference.

Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed

CoRR, 2018

Implicit Reparameterization Gradients.

Mikhail Figurnov, Shakir Mohamed, Andriy Mnih

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Implicit Generative Models with the Method of Learned Moments.

Suman V. Ravuri, Shakir Mohamed, Mihaela Rosca, Oriol Vinyals

Proceedings of the 35th International Conference on Machine Learning, 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 J. Goodfellow

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Variational Approaches for Auto-Encoding Generative Adversarial Networks.

Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed

CoRR, 2017

Generative Temporal Models with Memory.

Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo Jimenez Rezende, David Amos, Timothy P. Lillicrap

CoRR, 2017

The Cramer Distance as a Solution to Biased Wasserstein Gradients.

Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos

CoRR, 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

Proceedings of the 5th International Conference on Learning Representations, 2017

Recurrent Environment Simulators.

Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Learning in Implicit Generative Models.

Shakir Mohamed, Balaji Lakshminarayanan

CoRR, 2016

Early Visual Concept Learning with Unsupervised Deep Learning.

Irina Higgins, Loïc Matthey, Xavier Glorot, Arka Pal, Benigno Uria, Charles Blundell, Shakir Mohamed, Alexander Lerchner

CoRR, 2016

Normalizing Flows on Riemannian Manifolds.

Mevlana C. Gemici, Danilo Jimenez Rezende, Shakir Mohamed

CoRR, 2016

Unsupervised Learning of 3D Structure from Images.

Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter W. Battaglia, Max Jaderberg, Nicolas Heess

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.

Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.

Shakir Mohamed, Danilo Jimenez Rezende

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Variational Inference with Normalizing Flows.

Danilo Jimenez Rezende, Shakir Mohamed

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Partial Membership and Factor Analysis.

Zoubin Ghahramani, Shakir Mohamed, Katherine A. Heller

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

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

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

CoRR, 2014

Semi-supervised Learning with Deep Generative Models.

Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling

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.

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Adaptive Hamiltonian and Riemann Manifold Monte Carlo.

Ziyu Wang, Shakir Mohamed, Nando de Freitas

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

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

Mohammad Emtiyaz Khan, Shakir Mohamed, Benjamin M. Marlin, Kevin P. Murphy

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models.

David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando de Freitas

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression.

Mohammad Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy

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.

Marc Peter Deisenroth, Shakir Mohamed

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 .

Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Generalised Bayesian matrix factorisation models.

Shakir Mohamed

PhD thesis, 2011

Bayesian and L1 Approaches to Sparse Unsupervised Learning

Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani

CoRR, 2011

2009

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

Finale Doshi-Velez, David A. Knowles, Shakir Mohamed, Zoubin Ghahramani

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.

Mikkel N. Schmidt, Shakir Mohamed

Proceedings of the 17th European Signal Processing Conference, 2009

2008

Bayesian Exponential Family PCA.

Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

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

Shakir Mohamed, David M. Rubin, Tshilidzi Marwala

CoRR, 2007

Incremental Learning for Classification of Protein Sequences.

Shakir Mohamed, David M. Rubin, Tshilidzi Marwala

Proceedings of the International Joint Conference on Neural Networks, 2007

2006

Multi-class Protein Sequence Classification Using Fuzzy ARTMAP.

Shakir Mohamed, David M. Rubin, Tshilidzi Marwala

Proceedings of the IEEE International Conference on Systems, 2006

An Extension Neural Network and Genetic Algorithm for Bearing Fault Classification.

Shakir Mohamed, Thando Tettey, Tshilidzi Marwala

Proceedings of the International Joint Conference on Neural Networks, 2006