[Comp-neuro] DeepMath 2019 Call for Contributions
Ahmed El Hady
ahmed.imprs at gmail.com
Sun Apr 21 18:04:19 CEST 2019
*ANNOUNCEMENT AND CALL FOR CONTRIBUTIONS*
The 2019 Conference on Mathematical Theory of Deep Neural Networks
Princeton Club, New York City, Oct 31-Nov 1 2019.
======= Important Dates =======
Submission deadline for 1-page abstracts: June 28, 2019
Conference: Oct 31-Nov 1 2019.
======= Confirmed speakers =======
Anima Anandkumar (CalTech), Yasaman Bahri (Google), Minmin Chen (Google),
Michael Elad (Technion), Surya Ganguli (Stanford), Tomaso Poggio (MIT),
David Schwab (CUNY), Shai Shalev-Shwartz (Hebrew University),
Haim Sompolinsky (Hebrew University and Harvard), and Naftali Tishby
======= Workshop topic =======
Recent advances in deep neural networks (DNNs), combined with open,
easily-accessible implementations, have made DNNs a powerful, versatile
used widely in both machine learning and neuroscience. These advances in
practical results, however, have far outpaced a formal understanding of
networks and their training. Recently, long-past-due theoretical results
begun to emerge, shedding light on the properties of large, adaptive,
distributed learning architectures.
Following the success of the 2018 IAS-Princeton joint symposium on the same
topic (https://sites.google.com/site/princetondeepmath/home), the 2019
is more centrally located and broader in scope, but remains focused on
theoretical understanding of deep neural networks.
======= Call for abstracts =======
In addition to these high-profile invited speakers, we invite 1-page
non-archival abstract submissions. Abstracts will be reviewed
presented as posters.
To complement the wealth of conferences focused on applications, all
for DeepMath 2019 must target theoretical and mechanistic understanding
underlying properties of neural networks.
Insights may come from any discipline and we encourage submissions from
researchers working in computer science, engineering, mathematics,
physics, psychology, statistics, or related fields.
Topics may address any area of deep learning theory, including
computation, expressivity, generalization, optimization,
may apply to any or all network types including fully connected, recurrent,
convolutional, randomly connected, or other network topologies.
Ahmed El Hady
NYU: Joan Bruna
Columbia: Ashok Kumar
SUNY Stonybrook: Il Memming Park
Yale: Gal Mishne
City College: David Schwab
IAS: Nadav Cohen
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