248 citations · 306 across the 3 of their papers we have counts for
9 papers
Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin +1
Gradient-based methods for two-player games produce rich dynamics that can solve challenging problems, yet can be difficult to stabilize and understand. Part of this complexity ori…
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective
Florin Gogianu, Tudor Berariu, Mihaela Rosca +3
Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can…
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,…
Monte Carlo Gradient Estimation in Machine Learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov +1
This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the…
Deep Compressed Sensing
Yan Wu, Mihaela Rosca, Timothy Lillicrap
Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and re…
Training language GANs from Scratch
Cyprien de Masson d'Autume, Mihaela Rosca, Jack Rae +1
Generative Adversarial Networks (GANs) enjoy great success at image generation, but have proven difficult to train in the domain of natural language. Challenges with gradient estim…