Distributed Neural Networks@IJCNN 2016 : Special Session on Distributed Learning Algorithms for Neural Networks

in Conferences   Posted on October 16, 2015 

Conference Information

Submission Deadline Friday 15 Jan 2016 Proceedings indexed by :
Conference Dates Jul 25, 2016 - Jul 29, 2016
Conference Address Vancouver (Canada), Canada
Conference & Submission Link http://ispac.diet.uniroma1.it/ijcnn-2016-special-session-distributed-nn/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Call for Papers

[Apologies if you receive multiple copies of this CFP]

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Call for papers: IJCNN 2016 Special Session

DISTRIBUTED LEARNING ALGORITHMS FOR NEURAL NETWORKS

Vancouver, Canada, 25-29 July 2016

http://ispac.diet.uniroma1.it/ijcnn-2016-special-session-distributed-nn

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Scope and motivations

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In the era of big data and pervasive computing, it is common that datasets are distributed over multiple and geographically distinct sources of information (e.g. distributed databases). In this respect, a major challenge is designing adaptive training algorithms in a distributed fashion, with only partial or no reliance on a centralized authority. Indeed, distributed learning is an important step to handle inference within several research areas, including sensor networks, parallel and commodity computing, distributed optimization, and many others.

Based on the idea that all the aforementioned research fields share many fundamental questions and mechanisms, this special session is intended to bring forth advances on distributed training for neural networks. We are interested in papers proposing novel algorithms and protocols for distributed training under multiple constraints, analyses of their theoretical aspects, and applications for multiple source data clustering, regression and classification.

Topics

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The topics of interest to be covered by this Special Session include, but are not limited to:

* Distributed algorithms for training neural networks and kernel methods

* Theoretical aspects of distributed learning (e.g. fundamental communication constraints)

* Learning on commodity computing architectures and parallel execution frameworks (e.g. MapReduce, Storm)

* Energy efficient distributed learning

* Distributed semi-supervised and active learning

* Novel results on distributed optimization for machine learning

* Cooperative and competitive multi-agent learning

* Learning in realistic wireless sensor networks

* Distributed systems with privacy concerns (e.g. healthcare systems)

Important dates

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* Paper submission deadline: January 15, 2016

* Notification of paper acceptance: March 15, 2016

* Camera-ready deadline: April 15, 2016

* Conference: July 25-29, 2016

Further details

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For additional details, please visit the special session\’s website, or contact one of the organizers:

Massimo Panella, Sapienza University of Rome (massimo [dot] panella [at] uniroma1 [dot] it).

Simone Scardapane, Sapienza University of Rome (simone [dot] scardapane [at] uniroma1 [dot] it).

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