Neural Compression of Atmospheric States.
CoRR, 2024
Corridor Geometry in Gradient-Based Optimization.
CoRR, 2024
On a continuous time model of gradient descent dynamics and instability in deep learning.
Trans. Mach. Learn. Res., 2023
Implicit regularisation in stochastic gradient descent: from single-objective to two-player games.
CoRR, 2023
Investigating the Edge of Stability Phenomenon in Reinforcement Learning.
CoRR, 2023
Why neural networks find simple solutions: The many regularizers of geometric complexity.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Discretization Drift in Two-Player Games.
Proceedings of the 38th International Conference on Machine Learning, 2021
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective.
Proceedings of the 38th International Conference on Machine Learning, 2021
Monte Carlo Gradient Estimation in Machine Learning.
J. Mach. Learn. Res., 2020
A case for new neural network smoothness constraints.
Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020
Training Language GANs from Scratch.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the 36th International Conference on Machine Learning, 2019
Distribution Matching in Variational Inference.
CoRR, 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
Variational Approaches for Auto-Encoding Generative Adversarial Networks.
CoRR, 2017
Sequence-to-sequence neural network models for transliteration.
CoRR, 2016