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

Robert Schapire schapire at CS.Princeton.EDU
Thu Mar 24 08:04:34 EDT 2011


also, we'll be taking the speaker out to lunch tomorrow (friday) 
12-1:30.  please send me email if you'd like to join us.

rob

On 3/23/2011 7:06 PM, David Blei wrote:
> 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.
>
> best
> dave
>
> ---
>
> 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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