[Colloquium] [TITLE & ABSTRACT CORRECTION] 2/17 Talk at TTIC: Alberto Bietti, Inria

[Jerome Allen]

*When:*   Monday, February 17th at *11:00am *

*Where:*   TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526



*Who: *     Alberto Bietti, Inria


*Title*: Foundations of deep convolutional models through kernel methods

*Abstract:*  Deep learning has been most widely successful in tasks where
the data presents a rich structure, such as images, audio, or text. The
choice of network architecture is believed to play a key role in exploiting
this structure, for instance through convolutions and pooling on natural
signals, yet a precise study of these properties and how they affect
learning guarantees is still missing. Another challenge for the theoretical
understanding of deep learning models is that they are often
over-parameterized and known to be powerful function approximators, while
being seemingly easy to optimize using gradient methods.
We study deep models through the lens of kernel methods, which naturally
define functional spaces for learning in a non-parametric manner, and have
links to optimization of infinitely-wide deep networks in certain regimes.
This allows us to study invariance and stability properties of various
convolutional architectures by studying the geometry of the kernel mapping,
as well as approximation properties of learning in different regimes.
Additionally, our kernel point of view defines a natural complexity
measure, the RKHS norm, which we leverage empirically to regularize deep
networks for improved generalization on small datasets, and for better
robustness to adversarial examples.

********************************************************************************************************

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*Jerome Allen*
Executive Assistant
*Toyota Technological Institute*
6045 S. Kenwood Avenue
Room 518
Chicago, IL  60637
p:(773) 702-2311
*jallen at ttic.edu <jallen at ttic.edu>*
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