Dear all,
After 5 Seasons of the One World Approximate Bayesian Computation (ABC) Seminarhttps://warwick.ac.uk/fac/sci/statistics/news/upcoming-seminars/abcworldseminar/owabc/, launched in April 2020 to gather members and disseminate results and innovation during those weeks and months under lockdown, we have now decided to launch a "new" seminar series, the One World Approximate Bayesian Inference (OWABI) Seminarhttps://warwick.ac.uk/fac/sci/statistics/news/upcoming-seminars/abcworldseminar/, to better reflect the broader interest and scope of this series, which goes beyond ABC. In particular, simulation-based inference and ML related techniques will play a crucial role. We are also please to announce that Stefan Radevhttps://faculty.rpi.edu/stefan-radev https://faculty.rpi.edu/stefan-radev has joined the OWABI Organiser Team.
The 1st OWABI talk will be given by Ullrich Koethehttps://hci.iwr.uni-heidelberg.de/vislearn/people/ullrich-koethe/ (University of Heidelberg), who will talk about "Free-form flows for physics-informed generative modeling" on Thursday the 31st October at 11am UK time.
Abstract: The talk first introduces a useful categorization of the (sometimes confusing) "generative model zoo" in terms of different change-of-variables formulas. It then shows how normalizing flows, a major architecture for generative neural networks, can be used for simulation-based Bayesian inference in the sciences. Finally, it proposes free-form flows to simplify the incorporation of physical prior knowledge, e.g. rotation and translation invariance or the restriction of the distribution to a manifold, into generative models. Keywords: normalizing flows; physical-informed neural networks; simulation-based inference The talk will be streamed on MS Teams: Join the meeting nowhttps://teams.microsoft.com/l/meetup-join/19%3adhZ_4e_XLNJzCXPAMzTvT6BZ5KShEETkd_wtTY52VI81%40thread.tacv2/1729665835412?context=%7b%22Tid%22%3a%2209bacfbd-47ef-4465-9265-3546f2eaf6bc%22%2c%22Oid%22%3a%2242c962d4-7e3d-42ae-ad13-21a387aadf72%22%7d Meeting ID: 394 858 339 716 Passcode: qLKarj We are looking forward to seeing you all, best, Massimiliano on the behalf of the OWABI Organisers
------ Dr. Massimiliano Tamborrino Reader (Associate Professor) and WIHEA Fellow Department of Statistics University of Warwick https://warwick.ac.uk/tamborrino