PARLEARNING 2012 - Workshop on Parallel and Distributed Computing for Machine Learning and Inference Problems

Website | Edit Freely

Category PARLEARNING 2012

Deadline: January 18, 2012 | Date: May 25, 2012

Venue/Country: Shanghai, China

Updated: 2011-12-28 12:23:02 (GMT+9)

Call For Papers - CFP

The Call-for-Paper for ParLearning 2012

Deadline is EXTENDED to 1/18/2012

ParLearning 2012

Workshop on Parallel and Distributed Computing

for Machine Learning and Inference Problems

May 25, 2012

Shanghai, China

In Conjunction with IPDPS 2012

https://researcher.ibm.com/researcher/view_project.php?id=2591

HIGHLIGHTS

* Foster collaboration between HPC community and AI community

* Applying HPC techniques for learning problems

* Identifying HPC challenges from learning and inference

* Explore a critical emerging area with strong academia and industry interest

* Great opportunity for researchers worldwide for collaborating with Chinese Academia and Industry

CALL FOR PAPERS

This workshop is one of the major meetings for bringing together researchers in High Performance Computing and Artificial Intelligence to discuss state-of-the-art algorithms, identify critical applications that benefit from parallelization, prospect research areas that require most convergence and assess the impact on broader technical landscape. This is also a great opportunity for researchers worldwide for collaborating with Chinese Academia and Industry.

Authors are invited to submit manuscripts of original unpublished research that demonstrate a strong interplay between parallel/distributed computing techniques and learning/inference applications, such as algorithm design and libraries/framework development on multicore/ manycore architectures, GPUs, clusters, supercomputers, cloud computing platforms that target applications including but not limited to:

Learning and inference using large scale Bayesian Networks

Large scale inference algorithms using parallel TPIC models, clustering and SVM etc.

Parallel natural language processing (NLP).

Semantic inference for disambiguation of content on web or social media

Discovering and searching for patterns in audio or video content

On-line analytics for streaming text and multimedia content

Comparison of various HPC infrastructures for learning

Large scale learning applications in search engine and social networks

Distributed machine learning tools (e.g., Mahout and IBM parallel tool)

Real-time solutions for learning algorithms on parallel platforms

IMPORTANT DATE

Workshop Paper Due January 18, 2012 [extended]

Author Notification February 1, 2012

Camera-ready Paper Due February 21, 2012

PAPER GUIDELINES

Submitted manuscripts may not exceed 10 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references. More format requirements will be posted on the IPDPS web page (www.ipdps.org) shortly after the author notification Authors can purchase up to 2 additional pages for camera-ready papers after acceptance. Please find details on www.ipdps.org. All papers must be submitted through the EDAS portal. Students with accepted papers have a chance to apply for a travel award. Please find details at www.ipdps.org.

Submit your paper using EDAS portal for ParLearning: http://edas.info/N11575

PROCEEDINGS

All papers accepted by the workshop will be included in the proceedings of the IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum (IPDPSW), indexed in EI and possibly in SCI.

ORGANIZATION

General Co-chairs:

Sutanay Choudhury, Pacific Northwest National Laboratory, USA

George Chin, Pacific Northwest National Laboratory, USA

Yinglong Xia, IBM T.J. Watson Research Center, USA

Local Chair:

Yihua Huang, Nanjing University, China

Program Co-chairs:

John Feo, Pacific Northwest National Laboratory, USA

Chandrika Kamath, Lawrence Livermore National Laboratory, USA

Anshul Gupta, IBM T.J. Watson Research Center, USA

Program Committee:

Arindam Banerjee, University of Minnesota, USA

Enhong Chen, Univ. of Sci. & Tech. of China, China

Weizhu Chen, Microsoft Research, China

Jatin Chhugani, Intel Corp., USA

Edmond Chow, Georgia Tech, USA

Tina Eliassi-Rad, Rutgers University, USA

Mahantesh Halappanavar, Pacific Northwest National Lab, USA

Lawrence B. Holder, Washington State U., USA

Yihua Huang, Nanjing University, China

Yan Liu, University of Southern California, USA

Arindam Pal, Indian Institute of Technology, India

Yangqiu Song, Microsoft Research, China

Oreste Villa, Pacific Northwest National Lab, USA

Jun Wang, IBM T.J. Watson Research Center, USA

Yi Wang, Tencent Holdings Lt., China

Haixun Wang, Microsoft Research, China

Lexing Xie, Australian National University, Australia

KEYNOTE SPEAKER

Dr. Haixun Wang

Microsoft Research, China

CONTACT

Should you have any questions regarding the workshop or this webpage, please contact yxia ~AT~ us DOT ibm DOT com, or sutanay DOT choudhury ~AT~ pnnl DOT gov.