6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning 2017)

Orlando, Florida, USA 

May 29 or June 2, 2017

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.

Topics of Interest :

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 13, 2017
• Notification: February 10, 2017
• Camera Ready: March 10, 2017

User Name : jerish
Posted 25-11-2016 on 09:57:22 AEDT

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