Big Learning: Algorithms, Systems, and Tools for Learning at Scale

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Deadline: September 30, 2011 | Date: December 16, 2011-December 17, 2011

Venue/Country: Granada, Spain

Updated: 2011-09-02 07:08:29 (GMT+9)

Call For Papers - CFP

Hardware Accelerated Learning: Practicality and performance of specialized high-performance hardware (e.g. GPUs, FPGAs, ASIC) for machine learning applications.

Applications of Big Learning: Practical application case studies; insights on end-users, typical data workflow patterns, common data characteristics (stream or batch); trade-offs between labeling strategies (e.g., curated or crowd-sourced); challenges of real-world system building.

Tools, Software, & Systems: Languages and libraries for large-scale parallel or distributed learning. Preference will be given to approaches and systems that leverage cloud computing (e.g. Hadoop, DryadLINQ, EC2, Azure), scalable storage (e.g. RDBMs, NoSQL, graph databases), and/or specialized hardware (e.g. GPU, Multicore, FPGA, ASIC).

Models & Algorithms: Applicability of different learning techniques in different situations (e.g., simple statistics vs. large structured models); parallel acceleration of computationally intensive learning and inference; evaluation methodology; trade-offs between performance and engineering complexity; principled methods for dealing with large number of features;

Submissions should be written as extended abstracts, no longer than 4 pages (excluding references) in the NIPS latex style. Relevant work previously presented in non-machine-learning conferences is strongly encouraged. Exciting work that was recently presented is allowed, provided that the extended abstract mentions this explicitly.

Submission Deadline: September 30th, 2011.

Please refer to the website for detailed submission instructions: http://biglearn.org/index.php/AuthorInfo