Archimedes Seminar Series - An alternative view of denoising diffusion models

Dr Francis Bach (Inria, Ecole Normale Supérieure, France)

Abstract

Denoising diffusion models have led to impressive generative models in many domains. In this talk, I will present recent progress, with a focus on formulations that do not involve stochastic differential equations.

Short Biography
Francis Bach is a researcher at Inria, leading since 2011 the machine learning team that is part of the Computer Science department at Ecole Normale Supérieur. He graduated from Ecole Polytechnique in 1997 and completed his Ph.D. in Computer Science at U.C. Berkeley in 2005, working with Professor Michael Jordan. He spent two years in the Mathematical Morphology group at Ecole des Mines de Paris, then he joined the computer vision project-team at Inria/Ecole Normale Supérieur from 2007 to 2010.Francis Bach is primarily interested in machine learning, especially in sparse methods, kernel-based learning, neural networks, and large-scale optimization. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council, and received the Inria young researcher prize in 2012, the ICML test-of-time Award in 2014 and 2019, the NeurIPS test-of-time award in 2021, as well as the Lagrange Prize in continuous optimization in 2018, and the Jean-Jacques Moreau prize in 2019. He was elected in 2020 at the French Academy of Sciences. In 2015, he was program co-chair of the International Conference in Machine Learning (ICML), general chair in 2018, and president of its board between 2021 and 2023; he was co-editor-in-chief of the Journal of Machine Learning Research between 2018 and 2023.