no code implementations • 22 Nov 2021 • Anna Kerekes, Anna Mészáros, Ferenc Huszár
In gradient descent, changing how we parametrize the model can lead to drastically different optimization trajectories, giving rise to a surprising range of meaningful inductive biases: identifying sparse classifiers or reconstructing low-rank matrices without explicit regularization.
no code implementations • 19 Oct 2022 • Szilvia Ujváry, Zsigmond Telek, Anna Kerekes, Anna Mészáros, Ferenc Huszár
Sharpness-aware minimization (SAM) aims to improve the generalisation of gradient-based learning by seeking out flat minima.
no code implementations • 16 May 2023 • Francisco Vargas, Teodora Reu, Anna Kerekes
Denoising diffusion models are a class of generative models which have recently achieved state-of-the-art results across many domains.