Guest
Speaker: Efstratios Gavves, University of Amsterdam Slides: of Time and Dynamics with Emphasis on In the past decades, the impressive progress in machine learning and applications -like computer vision- was mainly by assuming (or enforcing) that data is static and usually of spatial-only nature, that data is , that learning correlations suffices for high predictive accuracies. In the real world, however, data and processes are typically (spatio-) temporal, dynamic, non-stationary, non-iid, causal. This leads to paradoxical situations for learning algorithms. In this talk, I will first present my vision for a new type of learning that embraces temporality and dynamics. I will then discuss recent work that connects complexity in deep stochastic models, like hierarchical VAEs, with phase transitions, pointing perhaps to a link to statistical physics. I will continue with discussing how simpl
