An Extension Neural Network and Genetic Algorithm for Bearing Fault Classification. - Shakir Mohamed; Thando Tettey; Tshilidzi Marwala (IJCNN, 2006)

Multi-class Protein Sequence Classification Using Fuzzy ARTMAP. - Shakir Mohamed; David M. Rubin; Tshilidzi Marwala (SMC, 2006)

Incremental Learning for Classification of Protein Sequences. - Shakir Mohamed; David M. Rubin; Tshilidzi Marwala (IJCNN, 2007)

An Adaptive Strategy for the Classification of G-Protein Coupled Receptors - Shakir Mohamed; David M. Rubin; Tshilidzi Marwala (CoRR, 2007)

Bayesian Exponential Family PCA. - Shakir Mohamed; Katherine A. Heller; Zoubin Ghahramani (NIPS, 2008)

Probabilistic non-negative tensor factorization using Markov chain Monte Carlo. - Mikkel N. Schmidt; Shakir Mohamed (EUSIPCO, 2009)

Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. - Finale Doshi-Velez; David A. Knowles; Shakir Mohamed; Zoubin Ghahramani (NIPS, 2009)

Bayesian and L1 Approaches to Sparse Unsupervised Learning - Shakir Mohamed; Katherine A. Heller; Zoubin Ghahramani (CoRR, 2011)

Generalised Bayesian matrix factorisation models. - Shakir Mohamed (2011)

Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning . - Shakir Mohamed; Katherine A. Heller; Zoubin Ghahramani (ICML, 2012)

Expectation Propagation in Gaussian Process Dynamical Systems. - Marc Peter Deisenroth; Shakir Mohamed (NIPS, 2012)

Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression. - Mohammad Emtiyaz Khan; Shakir Mohamed; Kevin P. Murphy (NIPS, 2012)

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models. - David Buchman; Mark Schmidt 001; Shakir Mohamed; David Poole 001; Nando de Freitas (AISTATS, 2012)

A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. - Mohammad Emtiyaz Khan; Shakir Mohamed; Benjamin M. Marlin; Kevin P. Murphy (AISTATS, 2012)

Adaptive Hamiltonian and Riemann Manifold Monte Carlo. - Ziyu Wang 001; Shakir Mohamed; Nando de Freitas (ICML (3), 2013)

Stochastic Backpropagation and Approximate Inference in Deep Generative Models. - Danilo Jimenez Rezende; Shakir Mohamed; Daan Wierstra (ICML, 2014)

Semi-supervised Learning with Deep Generative Models. - Diederik P. Kingma; Shakir Mohamed; Danilo Jimenez Rezende; Max Welling (NIPS, 2014)

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models. - Danilo Jimenez Rezende; Shakir Mohamed; Daan Wierstra (CoRR, 2014)

Partial Membership and Factor Analysis. - Zoubin Ghahramani; Shakir Mohamed; Katherine A. Heller (Handbook of Mixed Membership Models and Their Applications, 2014)

Variational Inference with Normalizing Flows. - Danilo Jimenez Rezende; Shakir Mohamed (ICML, 2015)

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning. - Shakir Mohamed; Danilo Jimenez Rezende (NIPS, 2015)

One-Shot Generalization in Deep Generative Models. - Danilo Jimenez Rezende; Shakir Mohamed; Ivo Danihelka; Karol Gregor; Daan Wierstra (ICML, 2016)

Unsupervised Learning of 3D Structure from Images. - Danilo Jimenez Rezende; S. M. Ali Eslami; Shakir Mohamed; Peter W. Battaglia; Max Jaderberg; Nicolas Heess (NIPS, 2016)

Normalizing Flows on Riemannian Manifolds. - Mevlana C. Gemici; Danilo Jimenez Rezende; Shakir Mohamed (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)

Learning in Implicit Generative Models. - Shakir Mohamed; Balaji Lakshminarayanan (CoRR, 2016)

Recurrent Environment Simulators. - Silvia Chiappa; Sébastien Racanière; Daan Wierstra; Shakir Mohamed (ICLR (Poster), 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 (ICLR (Poster), 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)

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)

Variational Approaches for Auto-Encoding Generative Adversarial Networks. - Mihaela Rosca; Balaji Lakshminarayanan; David Warde-Farley; Shakir Mohamed (CoRR, 2017)

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 (ICLR (Poster), 2018)

Learning Implicit Generative Models with the Method of Learned Moments. - Suman V. Ravuri; Shakir Mohamed; Mihaela Rosca; Oriol Vinyals (ICML, 2018)

Implicit Reparameterization Gradients. - Mikhail Figurnov; Shakir Mohamed; Andriy Mnih (NeurIPS, 2018)

Distribution Matching in Variational Inference. - Mihaela Rosca; Balaji Lakshminarayanan; Shakir Mohamed (CoRR, 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; et al. (CoRR, 2018)

Training Language GANs from Scratch. - Cyprien de Masson d'Autume; Shakir Mohamed; Mihaela Rosca; Jack W. Rae (NeurIPS, 2019)

A case for new neural network smoothness constraints. - Mihaela Rosca; Theophane Weber; Arthur Gretton; Shakir Mohamed (ICBINB@NeurIPS, 2020)

Levels of Analysis for Machine Learning. - Jessica B. Hamrick; Shakir Mohamed (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)

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence. - Shakir Mohamed; Marie-Therese Png; William Isaac 001 (CoRR, 2020)

Monte Carlo Gradient Estimation in Machine Learning. - Shakir Mohamed; Mihaela Rosca; Michael Figurnov; Andriy Mnih (J. Mach. Learn. Res., 2020)

Domesticating the techno-racial project. - Shakir Mohamed (Nat. Mach. Intell., 2020)

Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities. - Nenad Tomasev; Kevin R. McKee; Jackie Kay; Shakir Mohamed (AIES, 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; et al. (CoRR, 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)

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; et al. (Nat., 2021)

Power to the People? Opportunities and Challenges for Participatory AI. - Abeba Birhane; William Isaac 001; Vinodkumar Prabhakaran; Mark Diaz; Madeleine Clare Elish; Iason Gabriel; Shakir Mohamed (EAAMO, 2022)

se-Shweshwe Inspired Fashion Generation. - Lindiwe Brigitte Malobola; Negar Rostamzadeh; Shakir Mohamed (CoRR, 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; et al. (CoRR, 2022)

Machine learning and health need better values. - Marzyeh Ghassemi; Shakir Mohamed (npj Digit. Medicine, 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)

Understanding Deep Generative Models with Generalized Empirical Likelihoods. - Suman V. Ravuri; Mélanie Rey; Shakir Mohamed; Marc Peter Deisenroth (CVPR, 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)

Epistemic Injustice in Generative AI. - Jackie Kay; Atoosa Kasirzadeh; Shakir Mohamed (AIES (1), 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 (CHI, 2024)

A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians. - Piotr Mirowski; Juliette Love; Kory W. Mathewson; Shakir Mohamed (FAccT, 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. - Piotr W. Mirowski; Juliette Love; Kory W. Mathewson; Shakir Mohamed (CoRR, 2024)

Neural Compression of Atmospheric States. - Piotr Mirowski; David Warde-Farley; Mihaela Rosca; Matthew Koichi Grimes; Yana Hasson; Hyunjik Kim; Mélanie Rey; Simon Osindero; Suman V. Ravuri; Shakir Mohamed (CoRR, 2024)

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach. - Irina Jurenka; Markus Kunesch; Kevin R. McKee; Daniel Gillick; Shaojian Zhu; Sara Wiltberger; Shubham Milind Phal; Katherine L. Hermann; Daniel Kasenberg; Avishkar Bhoopchand; Ankit Anand; Miruna Pîslar; et al. (CoRR, 2024)

LearnLM: Improving Gemini for Learning. - Abhinit Modi; Aditya Srikanth Veerubhotla; Aliya Rysbek; Andrea Huber; Brett Wiltshire; Brian Veprek; Daniel Gillick; Daniel Kasenberg; Derek Ahmed; Irina Jurenka; James Cohan; Jennifer She; et al. (CoRR, 2024)

Probabilistic weather forecasting with machine learning. - Ilan Price; Alvaro Sanchez-Gonzalez; Ferran Alet; Tom R. Andersson; Andrew El-Kadi; Dominic Masters; Timo Ewalds; Jacklynn Stott; Shakir Mohamed; Peter W. Battaglia; Rémi R. Lam; Matthew Willson (Nat., 2025)

Evaluating Gemini in an arena for learning. - Abhinit Modi; Aditya Srikanth Veerubhotla; Aliya Rysbek; Andrea Huber; Ankit Anand; Avishkar Bhoopchand; Brett Wiltshire; Daniel Gillick; Daniel Kasenberg; Eleni Sgouritsa; Gal Elidan; Hengrui Liu; et al. (CoRR, 2025)

Contextualizing ancient texts with generative neural networks. - Yannis M. Assael; Thea Sommerschield; Alison Cooley; Brendan Shillingford; John Pavlopoulos; Priyanka Suresh; Bailey Herms; Justin Grayston; Benjamin Maynard; Nicholas Dietrich; Robbe Wulgaert; Jonathan Prag; et al. (Nat., 2025)