Connectionists: 2nd CFP for IJCNN18 Special Session on Parallelism on Machine Learning: Theory and Applications

[Verónica Bolón]

[Apologies if you receive multiple copies of this CFP]

2nd Call for papers: special session on “Parallelism in Machine Learning: Theory and Applications” at IJCNN 2018

International Joint Conference on Neural Networks, hosted at IEEE World Congress on Computational Intelligence (IEEE WCCI 2018)
8-13 July 2018, Rio de Janeiro, Brazil - http://www.ecomp.poli.br/~wcci2018/ <http://www.ecomp.poli.br/~wcci2018/>

Parallelism in Machine Learning: Theory and Applications

Organized by: Veronica Bolon-Canedo, Jorge Gonzalez-Dominguez, Amparo Alonso-Betanzos (University of A Coruña, Spain), Beatriz Remeseiro (University of Oviedo, Spain)


Machine learning (ML) is a prolific research area focused on the study and definition of algorithms able to learn from and make predictions on data. The current technologies and the use of Internet have revolutionized the way in which people acquire, store, or share data, resulting in huge amounts of information. In order to deal with massive volumes of data, ML techniques have to learn complex models with millions of parameters, increasing the computational cost involved.

Parallel programming and distributed learning are gaining attention in the last years as a mean to alleviate the effects of this extremely increase of computational cost. The efficient exploitation of High Performance Computing (HPC) resources such as multicore CPUs, hardware accelerators (GPUs, Intel Xeon Phi coprocessors, FPGAs, etc.), clusters or cloud-based systems can significantly accelerate many ML algorithms. This increase of speed allows ML users to either reduce the time needed for their applications or to search a larger space in the same period of time.  


We invite papers on both practical and theoretical issues about incorporating parallel and distributed approaches into machine learning problems. In particular, topics of interest include, but are not limited to:

Development of parallel machine learning algorithms on multicore and manycore architectures: multithreading, GPUs, Intel Xeon Phi coprocessor, FPGAs, etc.
Development of distributed machine learning algorithms.
Exploitation of cloud, grid and distributed-memory systems to accelerate machine learning algorithms: Spark, Hadoop, MPI, etc.
Scalability analysis of parallel and distributed methods for machine learning.
Performance comparison of parallel and distributed machine learning algorithms.
Deep learning models trained across multicore CPUs, GPUs or clusters of computers.
Applications: bioinformatics, medicine, multimedia, marketing, cyber security, etc.



Submitted papers will be reviewed according to the IJCNN reviewing process and will be evaluated on their scientific value: originality, correctness, and writing style.


IMPORTANT DATES:

Paper submission: 15th January 2018
Paper acceptance: 15th March 2018
Final paper submission: 1st May 2018
Early registration: 1st May 2018
IEEE WCCI 2018 conference: 8-13 July 2018



Verónica Bolón Canedo, PhD
Grupo LIDIA
Departamento de Computación
Facultad de Informática
Universidade da Coruña

Campus de Elviña, s/n
15071 - A Coruña, Spain

Phone: +34 981 167150 Ext. 6007
Fax: +34 981 167160
e-mail: veronica.bolon at udc.es <mailto:veronica.bolon at udc.es>
http://www.lidiagroup.org <http://www.lidiagroup.org/>



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