[Comp-neuro] DeepMath 2019 Call for Contributions

Ahmed El Hady ahmed.imprs at gmail.com
Sun Apr 21 18:04:19 CEST 2019




The 2019 Conference on Mathematical Theory of Deep Neural Networks 
(DeepMath 2019)

Princeton Club, New York City, Oct 31-Nov 1 2019.

Web: https://www.deepmath-conference.com/

======= Important Dates =======

Submission deadline for 1-page abstracts: June 28, 2019

Notification: TBA.

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 
(Hebrew University).

=======   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 
double-blind and

presented as posters.

To complement the wealth of conferences focused on applications, all 

for DeepMath 2019 must target theoretical and mechanistic understanding 
of the

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, 
representations, and

may apply to any or all network types including fully connected, recurrent,

convolutional, randomly connected, or other network topologies.

Committee Information:

Organizing committee:


    Ahmed El Hady


    Adam Charles


    Mikio Aoi


    Andrew Saxe


    Joan Bruna


    Michael Shvartsman


    Sebastian Musslick


    Stephen Keeley

Advisory committee


    Jonathan Cohen


    Daniel Lee


    Sebastian Seung


    Ted Willke

Local Committee


    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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