[Amath-seminars] Tomaso Poggio -- Boeing Colloquium (February 22, 4pm)

Bethany Lusch herwaldt at uw.edu
Thu Feb 15 15:40:31 PST 2018

Dear Colleagues,

We are pleased to invite you to the Applied Math Department's Boeing
Distinguished Colloquium delivered by Prof. Tomaso Poggio from MIT.

Tomaso Poggio is Eugene McDermott Professor in the Department of Brain and
Cognitive Sciences and at the Artificial Intelligence Laboratory. He is a
founding member of the McGovern Institute, and is also the director of the
Center for Brains, Minds, and Machines, a multi-institutional collaboration
headquartered at the McGovern Institute. He joined the MIT faculty in 1981,
after ten years at the Max Planck Institute for Biology and Cybernetics in
Tubingen, Germany. He received a Ph.D. in 1970 from the University of
Genoa. Poggio is a Foreign Member of the Italian Academy of Sciences and a
Fellow of the American Academy of Arts and Sciences. He was awarded the
2014 Swartz Prize for Theoretical and Computational Neuroscience.

Tomaso Poggio, Boeing Distinguished Colloquium
When: Thursday, February 22 at 4 PM
Where: Smith Hall 102

The talk will be followed by a reception in the Lewis hall lounge.

The title and abstract are given below:

Why and when can deep networks avoid the curse of dimensionality

In recent years, by exploiting machine learning— in which computers learn
to perform tasks from sets of training examples — artificial-intelligence
researchers have built impressive systems. Two of my former postdocs— Demis
Hassabis and Amnon Shashua— are behind the two main success stories of AI
so far: AlphaGo bettering the best human players at Go and Mobileye leading
the whole automotive industry towards vision-based autonomous driving.
There is, however, little in terms of a theory explaining why deep networks
work so well. In this talk I will review an emerging body of theoretical
results on deep learning including the conditions under which it can be
exponentially better than shallow learning. The class of deep convolutional
networks represent an important special case that avoids the curse of
dimensionality for the class of hierarchical locally compositional
functions. I will also sketch the vision of the NSF-funded, MIT-based
Center for Brains, Minds and Machines which strives to make progress on the
science of intelligence by combining machine learning and computer science
with neuroscience and cognitive science.

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