BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Lars Ruthotto (Emory University) DTSTART:20210518T121500Z DTEND:20210518T134500Z DTSTAMP:20260423T022603Z UID:MathDeep/5 DESCRIPTION:Title: An Introduction to Generative Modeling\nby Lars Ruthotto (Emory Unive rsity) as part of Mathematics of Deep Learning\n\n\nAbstract\nDeep generat ive models (DGM) are neural networks with many hidden layers trained to ap proximate complicated\, high-dimensional probability distributions from a finite number of samples. When trained successfully\, we can use the DGMs to estimate the likelihood of each observation and to create new samples f rom the underlying distribution. Developing DGMs has become one of the mos t hotly researched fields in artificial intelligence in recent years. The literature on DGMs has become vast and is growing rapidly.\nSome advances have even reached the public sphere\, for example\, the recent successes i n generating realistic-looking images\, voices\, or movies\; so-called dee p fakes.\n\nDespite these successes\, several mathematical and practical i ssues limit the broader use of DGMs: given a specific dataset\, it remains challenging to design and train a DGM and even more challenging to find o ut why a particular model is or is not effective. To help students contrib ute to this field\, this talk provides an introduction to DGMs and provide s a concise mathematical framework for modeling the three most popular app roaches: normalizing flows (NF)\, variational autoencoders (VAE)\, and gen erative adversarial networks (GAN). We illustrate the advantages and disad vantages of these basic approaches using numerical experiments. Our goal i s to enable and motivate the reader to contribute to this proliferating re search area. Our presentation also emphasizes relations between generative modeling and optimal transport.\n LOCATION:https://researchseminars.org/talk/MathDeep/5/ END:VEVENT END:VCALENDAR