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Output Representation Learning
Yuhong Guo · Dale Schuurmans · Richard Zemel · Samy Bengio · Yoshua Bengio · Li Deng · Dan Roth · Kilian Q Weinberger · Jason Weston · Kihyuk Sohn · Florent Perronnin · Gabriel Synnaeve · Pablo R Strasser · julien audiffren · Carlo Ciliberto · Dan Goldwasser

Mon Dec 09 07:30 AM -- 06:30 PM (PST) @ Harrah's Sand Harbor III
Event URL: https://sites.google.com/site/outputrepresentlearn2013/ »

Modern data analysis is increasingly facing prediction problems that have complex and high dimensional output spaces. For example, document tagging problems regularly consider large (and sometimes hierarchical) sets of output tags; image tagging problems regularly consider tens of thousands of possible output labels; natural language processing tasks have always considered complex output spaces. In such complex and high dimensional output spaces the candidate labels are often too specialized---leading to sparse data for individual labels---or too generalized---leading to complex prediction maps being required. In such cases, it is essential to identify an alternative output representation that can provide latent output categories that abstract overly specialized labels, specialize overly abstract labels, or reveal the latent dependence between labels.

There is a growing body of work on learning output representations, distinct from current work on learning input representations. For example, in machine learning, work on multi-label learning, and particularly output dimensionality reduction in high dimensional label spaces, has begun to address the specialized label problem, while work on output kernel learning has begun to address the abstracted label problem. In computer vision, work on image categorization and tagging has begun to investigate simple forms of latent output representation learning to cope with abstract semantic labels and large label sets. In speech recognition, dimensionality reduction has been used to identify abstracted outputs, while hidden CRFs have been used to identify specialized latent outputs. In information retrieval and natural language processing, discovering latent output specializations in complex domains has been an ongoing research topic for the past half decade.

The aim of this workshop is to bring these relevant research communities together to identify fundamental strategies, highlight differences, and identify the prospects for developing a set of systematic theory and methods for output representation learning. The target communities include researchers working on image tagging, document categorization, natural language processing, large vocabulary speech recognition, deep learning, latent variable modeling, and large scale multi-label learning.

Author Information

Yuhong Guo (Carleton University)
Dale Schuurmans (University of Alberta & Google Brain)
Richard Zemel (Vector Institute/University of Toronto)
Samy Bengio (Apple)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.

Li Deng (Citadel)
Dan Roth (University of Illinois)
Kilian Q Weinberger (Cornell University / ASAPP Research)
Jason Weston (Google Research)
Kihyuk Sohn (Google)
Florent Perronnin (Xerox)
Gabriel Synnaeve (Facebook AI Research)
Pablo R Strasser (University of Applied Sciences, Western Switzerland and University of Geneva)
julien audiffren (LIF)
Carlo Ciliberto (University College London)
Dan Goldwasser (UMD)

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