Deep Learning Summer School, Montreal 2016

Learning Deep Generative Models

author: Ruslan Salakhutdinov, Machine Learning Department, Carnegie Mellon University
published: Aug. 23, 2016, recorded: August 2016, views: 14402

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Description

In this tutorial I will discuss mathematical basics of many popular deep generative models, including Restricted Boltzmann Machines (RBMs), Deep Boltzmann Machines (DBMs), Helmholtz Machines, Variational Autoencoders (VAE) and Importance Weighted Autoencoders (IWAE). I will further demonstrate that these models are capable of extracting meaningful representations from high-dimensional data with applications in visual object recognition, information retrieval, and natural language processing.

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