2.5k citations · 9k across the 32 of their papers we have counts for
32 papers
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Suman Ravuri, Mélanie Rey, Shakir Mohamed +1
Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge. It is especially difficult for certain model cl…
GraphCast: Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson +15
Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute reso…
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
Suman Ravuri, Karel Lenc, Matthew Willson +17
Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socio-economic needs of many sectors reliant on weather-de…
A case for new neural network smoothness constraints
Mihaela Rosca, Theophane Weber, Arthur Gretton +1
How sensitive should machine learning models be to input changes? We tackle the question of model smoothness and show that it is a useful inductive bias which aids generalization,…
A review of radar-based nowcasting of precipitation and applicable machine learning techniques
Rachel Prudden, Samantha Adams, Dmitry Kangin +4
A 'nowcast' is a type of weather forecast which makes predictions in the very short term, typically less than two hours - a period in which traditional numerical weather prediction…
Levels of Analysis for Machine Learning
Jessica Hamrick, Shakir Mohamed
Machine learning is currently involved in some of the most vigorous debates it has ever seen. Such debates often seem to go around in circles, reaching no conclusion or resolution.…