ParLearning 2016 - The 5th International Workshop on Parallel and
Distributed
Computing for Large Scale Machine Learning and Big Data Analytics
http://parlearning.ecs.fullerton.edu/
May 27, 2016
Chicago, USA
in conjunction with
The 30th IEEE International Parallel & Distributed Processing Symposium
(IPDPS 2016)
http://www.ipdps.org/
May 23-27, 2016
Chicago Hyatt Regency
Chicago, Illinois, USA
**********************************************************************************************
Call for Papers
Scaling up machine-learning (ML), data mining (DM) and reasoning algorithms
from Artificial Intelligence (AI) for massive datasets is a major technical
challenge in the times of "Big Data". The past ten years has seen the rise
of multi-core and GPU based computing. In distributed computing, several
frameworks such as Mahout, GraphLab and Spark continue to appear to
facilitate scaling up ML/DM/AI algorithms using higher levels of
abstraction. We invite novel works that advance the trio-fields of ML/DM/AI
through development of scalable algorithms or computing frameworks. Ideal
submissions would be characterized as scaling up X on Y, where potential
choices for X and Y are provided below.
Scaling up
recommender systems
gradient descent algorithms
deep learning
sampling/sketching techniques
clustering (agglomerative techniques, graph clustering, clustering
heterogeneous data)
classification (SVM and other classifiers)
SVD
probabilistic inference (bayesian networks)
logical reasoning
graph algorithms and graph mining
On
Parallel architectures/frameworks (OpenMP, OpenCL, Intel TBB)
Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark etc.)
Organization
Charalampos Chelmis, University of Southern California, USA
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Arindam Pal, TCS Innovation Labs, India
Anand Panangadan, California State University, Fullerton, USA
Yinglong Xia, IBM T.J. Watson Research Center, USA
Program Committee
Danny Bickson, GraphLab Inc., USA
Zhihui Du, Tsinghua University, China
Dinesh Garg, IBM India Research Laboratory, India
Renato Porfirio Ishii, Federal University of Mato Grosso do Sul (UFMS),
Brazil
Ananth Kalyanaraman, Washington State University, USA
Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
Carson Leung, University of Manitoba, Canada
Debnath Mukherjee, TCS Innovation Labs, India
Arijit Mukherjee, TCS Innovation Labs, India
Francesco Parisi, University of Calabria, Italy
Himadri Sekhar Paul, TCS Innovation Labs, India
Gautam Shroff, TCS Innovation Labs, India
Aniruddha Sinha, TCS Innovation Labs, India
Jianting Zhang, City College of New York, USA
Important Dates
Paper submission: January 15, 2016 AoE
Notification: February 12, 2016
Camera Ready: February 26, 2016
Paper Guidelines
Submitted manuscripts may not exceed 6 single-spaced double-column pages
using 10-point size font on 8.5x11 inch pages (IEEE conference style),
including figures, tables, and references. Format requirements are posted
on the IEEE IPDPS web page.
All submissions must be uploaded electronically at
http://edas.info/newPaper.php?c=21782
Regards,
Dr. Arindam Pal
Research Scientist
Innovation Labs Kolkata
TCS Research
http://www.cse.iitd.ac.in/~arindamp/
**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zaik.uni-koeln.de/AFS/publications/dmanet/
*
**********************************************************
Posts
