Speaker |
Sebastian Nowozin |
|---|---|
Affiliation |
Microsoft Research |
Date |
Friday, 09 February 2018 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts Building G08 Sir David Davies LT |
Event series |
Jump Trading/ELLIS CSML Seminar Series |
Abstract |
Slides from the talk: http://www.nowozin.net/sebastian/talks/nowozin-london-2018-02-09.pptx Generative Adversarial Networks (GANs) have breathed new life into research on generative models. Generative models promise to be able to learn rich structural representations from unsupervised data, enabling data-efficient modelling in complex domains. The talk is divided into three parts. The first part introduces the basic GAN approach, understanding it both on the statistical level in terms of minimizing a divergence between probability distributions and algorithmically in terms of a smooth two-player game. The second part discusses problems in the GAN approach and consolidates recent research by highlighting problems both in the statistical viewpoint (existence of divergences) and in the algorithmic viewpoint (convergence of the GAN game), making recommendations for practical use of GAN models. The third part discusses the relationship to other generative modelling approaches, potential applications of GANs and GAN-type approximations, and raises open problems for future research. |
Biography |