[Ml-stat-talks] Ruslan Salakhutdinov, Tomorrow 3/24, 4:30PM, CS105

David Blei blei at CS.Princeton.EDU
Wed Mar 23 19:06:03 EDT 2011

hi ml-stat-talks

this is not to be missed, especially if you are enthusiastic about any
of the following:

(a) graphical models
(b) bayesian methods
(c) deep learning
(d) approximate inference
(e) unsupervised learning

if you are planning to be hungry, there will be snacks in the CS tea
room (2nd floor) at 4pm.



Learning Hierarchical Generative Models
Ruslan Salakhutdinov, Massachusetts Institute of Technology
Thursday, March 24, 4:30pm
Computer Science 105

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.

Ruslan Salakhutdinov received his PhD in computer science from the
University of Toronto in 2009, and he is now a postdoctoral associate
at CSAIL and 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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