This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent variable models: invertible models and intractable models. Special emphasis is placed on understanding variational inference as a key to training intractable latent variable models.
Note this lecture was originally advertised as lecture 9.
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Andriy Mnih is a Research Scientist at DeepMind. He works on generative modelling, representation learning, variational inference, and gradient estimation for stochastic computation graphs. He did his PhD on learning representations of discrete data at the University of Toronto, where he was advised by Geoff Hinton. Prior to joining DeepMind, Andriy was a post-doctoral researcher at the Gatsby Unit, University College London, working with Yee Whye Teh.
About the lecture series:
The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning.
In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.