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Igor Babuschkin, DeepMind, OH 14, E23

Event Date: June 13, 2019 16:15


Generative Models

Abstract:
Generative models are a set of unsupervised learning techniques, which
attempt to model the distribution of the data points themselves
instead of predicting labels from them. In recent years, deep learning
approaches to generative models have produced impressive results in
areas such as modeling of images (BigGAN), audio (WaveNet), language
(Transformer, GPT-2) and others. I'm going to give an overview of the
three most popular underlying methods used in deep generative models
today: Autoregressive models, generative adversarial networks and
variational autoencoders. I will also go over some of the state of the
art models and explain how they work.

CV:
Igor Babuschkin is a Senior Research Engineer at DeepMind, Google's
artificial intelligence division with the ambitious goal of building a
general artificial intelligence. He studied physics at the TU Dortmund
(2010-2015), where he was involved in experimental particle physics
research at the LHCb experiment at CERN. He then switched fields to
machine learning and artificial intelligence, joining DeepMind in
2017. Since then he has been working on new types of generative models
and approaches to scalable deep reinforcement learning. He is a tech
lead of DeepMind's AlphaStar project, which recently produced the
first software agent capable of beating a professional player at the
game of StarCraft II.



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