Learning Hierarchical Generative Models
Speaker: Ruslan Salakhutdinov , M.I.T.
Date: March 31 2011
Time: 4:00PM to 5:00PM
Location: 32-G449
Host: Regina Barzilay and Leslie Kaelbling , CSAIL
Contact: Francis Doughty, 253-4602, doughty@mit.edu
Building intelligent systems that are capable of extracting meaningful
representations from high-dimensional data lies at the core of solving
many Artificial Intelligence tasks, including visual object recognition,
information retrieval, speech perception, and language understanding. My
research aims to discover such representations by learning rich generative
models which contain deep hierarchical structure and which support
inferences at multiple levels.
In this talk, I will introduce a broad class of probabilistic generative
models called Deep Boltzmann Machines (DBMs), and a new algorithm for
learning these models that uses variational methods and Markov chain Monte
Carlo. I will show that DBMs can learn useful hierarchical representations
from large volumes of high-dimensional data, and that they can be
successfully applied in many domains, including information retrieval,
object recognition, and nonlinear dimensionality reduction. I will then
describe a new class of more complex probabilistic graphical models that
combine Deep Boltzmann Machines with structured hierarchical Bayesian
models. I will show how these models can learn a deep hierarchical
structure for sharing knowledge across hundreds of visual categories,
which allows accurate learning of novel visual concepts from few examples.
BIO: Ruslan Salakhutdinov received his PhD in computer science from the
University of Toronto in 2009, and he is now a postdoctoral associate at
the Department of Brain and Cognitive Sciences at MIT. His research
interests lie in machine learning, computational statistics, and
large-scale optimization. He is the recipient of the NSERC Postdoctoral
Fellowship and Canada Graduate Scholarship.
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