Our first guest speaker is Emiel Hoogeboom from the University of Amsterdam and you are all cordially invited to the CVG Seminar on March 26th at 1:30 p.m. CET on Zoom (passcode is 809447), where‪ Emiel will give a talk titled “Distributions and Geometry“.

Abstract

Deep generative models aim to model complicated high-dimensional distributions. Among these are Normalizing Flows, a rich family of distributions for many different types of geometry. Normalizing Flows are attractive because in many cases they admit exact likelihood evaluation, and can be designed for fast inference and sampling. Modelling high-dimensional distributions has many applications such as representation learning, outlier detection, variance reduction in estimator, and (conditional) generation. In this talk, we will visit applications of flows on hyperspheres and flows for discrete spaces. Additionally, we talk about graph neural networks with rotational and translational symmetries.

Bio

Emiel is a PhD Student at the University of Amsterdam, working on deep generative modelling under the supervision of Max Welling. Recent works include "Integer Discrete Flows", "Argmax Flows" and "E(n)-equivariant Graph Neural Networks"