7 citations · 31 across the 15 of their papers we have counts for
3 papers · 1 filter
Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data
Jérémie Klinger, Raphaël Urfin, Giulio Biroli +1
Score-based generative models generate new samples by integrating a time-dependent drift that carries Gaussian noise onto the target distribution. In practice this drift is modeled…
The critical slowing down in training diffusion models
Luca Maria Del Bono, Giulio Biroli, Patrick Charbonneau +1
Computational sampling has been central to the sciences since the mid-20th century. While machine-learning-based approaches have recently enabled major advances, their behavior rem…
Annealing in variational inference mitigates mode collapse: A theoretical study on Gaussian mixtures
Luigi Fogliani, Bruno Loureiro, Marylou Gabrié
Mode collapse, the failure to capture one or more modes when targetting a multimodal distribution, is a central challenge in modern variational inference. In this work, we provide…