# Applied Math Seminar

Date:

10/24/2019 - 11:00am to 12:00pm

Location:

POT 745

Speaker(s) / Presenter(s):

Vasily Zadorozhnyy, University of Kentucky

**Title:**Generative Adversarial Networks

**Abstract:**In 2014, Ian Goodfellow et al. proposed a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. In this talk, I will talk about the structure of such a framework, how we train it as well as some theoretical results and applications.

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