Tuesday, 13th February 2024 Self-supervised learning for Bayesian experimental design - Michael Gutmann Abstract: The natural sciences increasingly use machine learning for data analysis. Whether the analysis of the data is successful or not, however, ultimately depends on the quality of the data; a sophisticated analysis cannot fix uninformative data. In this talk, I will show that machine learning can help not only with data analysis but also with the design of experiments to collect informative data. I will focus on methods that we have developed for the large class of simulator models which includes models of biochemical reactions, neural activity or infectious diseases to name a few examples. Abstract: Event type: Workshop Date: Tuesday, 13th February Time: 11:00 Location: G.03 Speaker(s): Michael Gutmann, Javier Alfaro Chair/Host: Chenfei Ma This article was published on 2024-11-22
