Shakir Mohamed

Orcid: 0000-0002-1184-5776

Affiliations:
  • DeepMind, UK


According to our database1, Shakir Mohamed authored at least 60 papers between 2006 and 2024.

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Bibliography

2024
Epistemic Injustice in Generative AI.
CoRR, 2024

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach.
CoRR, 2024

Neural Compression of Atmospheric States.
CoRR, 2024

A Robot Walks into a Bar: Can Language Models Serve as Creativity Support Tools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians.
CoRR, 2024

A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians.
Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

The Illusion of Artificial Inclusion.
Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

2023
GenCast: Diffusion-based ensemble forecasting for medium-range weather.
CoRR, 2023

Understanding Deep Generative Models with Generalized Empirical Likelihoods.
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.
Trans. Mach. Learn. Res., 2022

Machine learning and health need better values.
npj Digit. Medicine, 2022

GraphCast: Learning skillful medium-range global weather forecasting.
CoRR, 2022

se-Shweshwe Inspired Fashion Generation.
CoRR, 2022

Power to the People? Opportunities and Challenges for Participatory AI.
Proceedings of the Equity and Access in Algorithms, Mechanisms, and Optimization, 2022

2021
Skilful precipitation nowcasting using deep generative models of radar.
Nat., 2021

Normalizing Flows for Probabilistic Modeling and Inference.
J. Mach. Learn. Res., 2021

Skillful Precipitation Nowcasting using Deep Generative Models of Radar.
CoRR, 2021

Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020
Domesticating the techno-racial project.
Nat. Mach. Intell., 2020

Monte Carlo Gradient Estimation in Machine Learning.
J. Mach. Learn. Res., 2020

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence.
CoRR, 2020

A review of radar-based nowcasting of precipitation and applicable machine learning techniques.
CoRR, 2020

Levels of Analysis for Machine Learning.
CoRR, 2020

A case for new neural network smoothness constraints.
Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019
Training Language GANs from Scratch.
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.
CoRR, 2018

Distribution Matching in Variational Inference.
CoRR, 2018

Implicit Reparameterization Gradients.
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.
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.
Proceedings of the 6th International Conference on Learning Representations, 2018

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

Generative Temporal Models with Memory.
CoRR, 2017

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

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework.
Proceedings of the 5th International Conference on Learning Representations, 2017

Recurrent Environment Simulators.
Proceedings of the 5th International Conference on Learning Representations, 2017

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 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.
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

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


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