Hi all,
I was contacted earlier this week by one of the GraphLab authors, and he
wanted me to forward news of the following workshop on to the Mahout
group, along with a word of encouragement to submit to the presentation.
Joey Gonzalez (who contacted me) is one of the lead organizers this year:
"We are organizing the Big Learning NIPS workshop this year (and it is
really big with 2 days and 14 organizers spanning academia and
industry). This workshop is supposed to bring people from academia and
industry together to discuss the state of the art in large-scale
learning. I would like to encourage people from the Mahout team as
well as Mahout users to consider both attending and even submitting
brief abstracts describing their current tools, software, algorithms,
and even large scale problems."
It's in Spain at the end of the year.
Shannon
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Big Learning: Algorithms, Systems, and Tools for Learning at Scale
NIPS 2011 Workshop (http://www.biglearn.org <http://www.biglearn.org/>)
Submissions are solicited for a two day workshop December 16-17 in
Sierra Nevada, Spain.
This workshop will address tools, algorithms, systems, hardware, and
real-world problem domains related to large-scale machine learning (“Big
Learning”). The Big Learning setting has attracted intense interest with
active research spanning diverse fields including machine learning,
databases, parallel and distributed systems, parallel architectures, and
programming languages and abstractions. This workshop will bring
together experts across these diverse communities to discuss recent
progress, share tools and software, identify pressing new challenges,
and to exchange new ideas. Topics of interest include (but are not
limited to):
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
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Big Learning: Algorithms, Systems, and Tools for Learning at Scale
http://biglearn.org/
