Dates:
When May 25, 2012 – May 25, 2012
Where Shanghai, China
Submission Deadline Dec 19, 2011
Notification Due Feb 1, 2012
Final Version Due Feb 21, 2012
From the notice:
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 industry interest without overlapping with existing IPDPS workshops
- Great opportunity for researchers worldwide for collaborating with Chinese Academia and Industry
CALL FOR PAPERS
Authors are invited to submit manuscripts of original unpublished research that demonstrate a strong interplay between parallel/distributed computing techniques and learning/inference applications, such as algorithm design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
If you are wondering what role topic maps have to play in this arena, ask yourself the following question:
Will the systems and techniques demonstrated at this conference use the same means to identify the same subjects?*
If your answer is no, what would you suggest is the solution for mapping different identifications of the same subjects together?
My answer to that question is to use topic maps.
*Whatever your ascribe as its origin, semantic diversity is part and parcel of the human condition. We can either develop silos or maps across silos. Which do you prefer?