The 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics
May 27, 2016
http://parlearning.ecs.
In Conjunction with the 30th IEEE International Parallel & Distributed Processing Symposium, Chicago Hyatt Regency, Chicago, Illinois USA
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.)
IMPORTANT DATES
• Paper submission: January 15, 2016 AoE
• Notification: February 12, 2016
• Camera Ready: February 26, 2016
PAPER GUIDELINES
Submitted manuscripts shall be of 6-10 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 (www.ipdps.org).
Accepted papers with sufficient extensions will be recommended for publication in a journal (to be announced), subject to review by the journal editorial board. Best paper award is considered ($500 in 2015).
TRAVEL AWARDS
Students with accepted papers can apply for a travel award. Please find details at www.ipdps.org
May 27, 2016
http://parlearning.ecs.
In Conjunction with the 30th IEEE International Parallel & Distributed Processing Symposium, Chicago Hyatt Regency, Chicago, Illinois USA
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.)
IMPORTANT DATES
• Paper submission: January 15, 2016 AoE
• Notification: February 12, 2016
• Camera Ready: February 26, 2016
PAPER GUIDELINES
Submitted manuscripts shall be of 6-10 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 (www.ipdps.org).
Accepted papers with sufficient extensions will be recommended for publication in a journal (to be announced), subject to review by the journal editorial board. Best paper award is considered ($500 in 2015).
TRAVEL AWARDS
Students with accepted papers can apply for a travel award. Please find details at www.ipdps.org
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