The thirty-fifth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, October 21, 2021.
Bayesian methods can provide full-predictive distributions and well-calibrated uncertainties in modern deep learning. The Bayesian approach is especially relevant in scientific and healthcare applications—where we wish to have reliable predictive distributions for decision making, and the facility to naturally incorporate domain expertise. With a Bayesian approach, we not only want to find a single point that optimizes a loss, but rather to integrate over a loss landscape to form a Bayesian model average. The geometric properties of the loss surface, rather than the specific locations of optima, therefore greatly influence the predictive distribution in a Bayesian procedure. By better understanding loss geometry, we can realize the significant benefits of Bayesian methods in modern deep learning, overcoming challenges of dimensionality. In this talk, I review work on Bayesian inference and loss geometry in modern deep learning, including challenges, new opportunities, and applications.
Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Didier Clouteau (MSSMAT), Amélie Fau (LMT), Filippo Gatti (MSSMAT), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (DOTA), Fernando Lopez-Caballero (MSSMAT), Didier Lucor (LISN), Emmanuel Vazquez (L2S).
Coordinator: Julien Bect (L2S).
Practical details: the seminar will be held online using Microsoft Teams.
If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).
You will find the link to the seminar on the « General » UQSay channel on Teams, approximately 15 minutes before the beginning.
The technical side of things: you can use Teams either directly from your web browser or using the « fat client », which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.