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Workshop
Learning Semantics
Antoine Bordes · Jason E Weston · Ronan Collobert · Leon Bottou

Fri Dec 16 10:30 PM -- 11:00 AM (PST) @ Melia Sol y Nieve: Ski

A key ambition of AI is to render computers able to evolve in and interact with the real world. This can be made possible only if the machine is able to produce a correct interpretation of its available modalities (image, audio, text, etc.), upon which it would then build a reasoning to take appropriate actions. Computational linguists use the term semantics'' to refer to the possible interpretations (concepts) of natural language expressions, and showed some interest inlearning semantics'', that is finding (in an automated way) these interpretations. However, semantics'' are not restricted to natural language modality, and are also pertinent for speech or vision modalities. Hence, knowing visual concepts and common relationships between them would certainly bring a leap forward in scene analysis and in image parsing akin to the improvement that language phrase interpretations would bring to data mining, information extraction or automatic translation, to name a few.

Progress in learning semantics has been slow mainly because this involves sophisticated models which are hard to train, especially since they seem to require large quantities of precisely annotated training data. However, recent advances in learning with weak and limited supervision lead to the emergence of a new body of research in semantics based on multi-task/transfer learning, on learning with semi/ambiguous supervision or even with no supervision at all. The goal of this workshop is to explore these new directions and, in particular, to investigate the following questions:
\begin{itemize}
\item How should meaning representations be structured to be easily interpretable by a computer and still express rich and complex knowledge?
\item What is a realistic supervision setting for learning semantics? How can we learn sophisticated representations with limited supervision?
\item How can we jointly infer semantics from several modalities?

This workshop defines the issue of learning semantics as its main interdisciplinary subject and aims at identifying, establishing and discussing potential, challenges and issues of learning semantics. The workshop is mainly organized around invited speakers to highlight several key current directions, but, it also presents selected contributions and is intended to encourage the exchange of ideas with all the other members of the NIPS community.

#### Author Information

##### Jason E Weston (Facebook AI Research)

Jason Weston received a PhD. (2000) from Royal Holloway, University of London under the supervision of Vladimir Vapnik. From 2000 to 2002, he was a researcher at Biowulf technologies, New York, applying machine learning to bioinformatics. From 2002 to 2003 he was a research scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. From 2004 to June 2009 he was a research staff member at NEC Labs America, Princeton. From July 2009 onwards he has been a research scientist at Google, New York. Jason Weston's current research focuses on various aspects of statistical machine learning and its applications, particularly in text and images.

##### Ronan Collobert (Facebook)

Ronan Collobert received his master degree in pure mathematics from University of Rennes (France) in 2000. He then performed graduate studies in University of Montreal and IDIAP (Switzerland) under the Bengio brothers, and received his PhD in 2004 from University of Paris VI. He joined NEC Labs (USA) in January 2005 as a postdoc, and became a research staff member after about one year. His research interests always focused on large-scale machine-learning algorithms, with a particular interest in semi-supervised learning and deep learning architectures. Two years ago, his research shifted in the natural language processing area, slowly going towards automatic text understanding.

##### Leon Bottou (Facebook AI Research)

Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .