[Comp-neuro] DeepMath 2019 Call for Contributions

[Ahmed El Hady]

*ANNOUNCEMENT AND CALL FOR CONTRIBUTIONS*

**

*

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 
method

used widely in both machine learning and neuroscience. These advances in

practical results, however, have far outpaced a formal understanding of 
these

networks and their training. Recently, long-past-due theoretical results 
have

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 
meeting

is more centrally located and broader in scope, but remains focused on 
rigorous

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 
submissions

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, 
neuroscience,

physics, psychology, statistics, or related fields.


Topics may address any area of deep learning theory, including 
architectures,

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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