[Lisa_seminaires] [Tea Talk] David Rolnick (UPenn) August 24 2018 10:30 AA6214

Michael Noukhovitch mnoukhov at gmail.com
Jeu 23 Aou 12:56:05 EDT 2018


Sorry for the delay but tomorrow we have *David Rolnick * visiting with
Konrad Kording's lab at * UPenn * giving a talk on *August 24 2018* at
*10:30* in room *AA6214*

**Note the different room AA6214**

Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>?
yes
David will be free until only 1:30~2pm so if you'd like to go with him for
lunch, stay after the talk and join in!

Be sure to come and hear about the impactful, *profound* insights David has
to share.
Michael

P.S. since David was so kind as to step in on short notice, I'll be making
(or buying) cookies for early attendees ;)

*TITLE* The impact of depth on expressivity and learning

*KEYWORDS *Deep Learning Theory

*ABSTRACT*
Deeper networks are more powerful than shallow ones, but can be harder to
train. In this talk, we will rigorously investigate why both of these
statements are true. Specifically, we will prove that depth leads to an
exponentially greater ability to express even simple polynomial functions.
We will identify why some initializations and architectures impede learning
in deeper networks, and demonstrate (both mathematically and empirically)
several principles to bear in mind when designing an MLP/ConvNet/ResNet
that will learn effectively.

*BIO*
David Rolnick completed his Ph.D. in Mathematics at MIT this year,
co-advised by Nir Shavit, Max Tegmark, and Ed Boyden. His work focuses on
the mathematical foundations of artificial and biological neural networks.
A former NSF Graduate Research Fellow and Fulbright Scholar, David has also
worked on machine learning research as an intern at Google and DeepMind. He
will be joining Konrad Kording's group at UPenn as an NSF Mathematical
Sciences Postdoctoral Research Fellow.
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