Timezone: »
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 in
learning 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
Antoine Bordes (CNRS / U. Tech. de Compiègne)
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 .
More from the Same Authors
-
2020 : Invited Talk 4 Presentation - Jason Weston - (Towards) Learning from Conversing »
Jason E Weston -
2021 Spotlight: Hash Layers For Large Sparse Models »
Stephen Roller · Sainbayar Sukhbaatar · arthur szlam · Jason Weston -
2021 : On the Relation between Distributionally Robust Optimization and Data Curation »
Agnieszka Słowik · Leon Bottou -
2021 : On the Relation between Distributionally Robust Optimization and Data Curation »
Agnieszka Słowik · Leon Bottou -
2021 : Poster: Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation »
Agnieszka Słowik · Leon Bottou -
2022 : Learning to Reason and Memorize with Self-Questioning »
Jack Lanchantin · Shubham Toshniwal · Jason E Weston · arthur szlam · Sainbayar Sukhbaatar -
2022 : Pre-train, fine-tune, interpolate: a three-stage strategy for domain generalization »
Alexandre Rame · Jianyu Zhang · Leon Bottou · David Lopez-Paz -
2022 : Continuous Soft Pseudo-Labeling in ASR »
Tatiana Likhomanenko · Ronan Collobert · Navdeep Jaitly · Samy Bengio -
2022 : Invited Keynote by Jason Weston »
Jason Weston -
2022 : Learning to Reason and Memorize with Self-Questioning »
Jack Lanchantin · Shubham Toshniwal · Jason E Weston · arthur szlam · Sainbayar Sukhbaatar -
2022 Poster: The Effects of Regularization and Data Augmentation are Class Dependent »
Randall Balestriero · Leon Bottou · Yann LeCun -
2022 Poster: Star Temporal Classification: Sequence Modeling with Partially Labeled Data »
Vineel Pratap · Awni Hannun · Gabriel Synnaeve · Ronan Collobert -
2022 Poster: Staircase Attention for Recurrent Processing of Sequences »
Da JU · Stephen Roller · Sainbayar Sukhbaatar · Jason E Weston -
2021 : Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation »
Agnieszka Słowik · Leon Bottou -
2021 Poster: Hash Layers For Large Sparse Models »
Stephen Roller · Sainbayar Sukhbaatar · arthur szlam · Jason Weston -
2021 Poster: CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings »
Tatiana Likhomanenko · Qiantong Xu · Gabriel Synnaeve · Ronan Collobert · Alex Rogozhnikov -
2020 Workshop: Wordplay: When Language Meets Games »
Prithviraj Ammanabrolu · Matthew Hausknecht · Xingdi Yuan · Marc-Alexandre Côté · Adam Trischler · Kory Mathewson @korymath · John Urbanek · Jason Weston · Mark Riedl -
2020 : Panel »
Maxine Eskenazi · Ankur Parikh · Govindarajan Thattai · Alexander Rudnicky · Jason E Weston -
2020 : Invited Talk 4 Q/A - Jason Weston »
Jason E Weston -
2020 Memorial: In Memory of Olivier Chapelle »
Bernhard Schölkopf · Andre Elisseeff · Olivier Bousquet · Vladimir Vapnik · Jason E Weston -
2019 Poster: Cold Case: The Lost MNIST Digits »
Chhavi Yadav · Leon Bottou -
2019 Spotlight: Cold Case: The Lost MNIST Digits »
Chhavi Yadav · Leon Bottou -
2018 : Teaching through Dialogue and Games »
Jason E Weston -
2018 : Humans and models as embodied dialogue agents in text-based games »
Jason Weston -
2018 : The Conversational Intelligence Challenge 2 (ConvAI2) : Setup, Opening Words »
Jason Weston -
2018 Workshop: Causal Learning »
Martin Arjovsky · Christina Heinze-Deml · Anna Klimovskaia · Maxime Oquab · Leon Bottou · David Lopez-Paz -
2018 Workshop: Smooth Games Optimization and Machine Learning »
Simon Lacoste-Julien · Ioannis Mitliagkas · Gauthier Gidel · Vasilis Syrgkanis · Eva Tardos · Leon Bottou · Sebastian Nowozin -
2018 Poster: SING: Symbol-to-Instrument Neural Generator »
Alexandre Defossez · Neil Zeghidour · Nicolas Usunier · Leon Bottou · Francis Bach -
2017 : Geometrical Insights for Unsupervised Learning »
Leon Bottou -
2017 : Looking for a Missing Signal »
Leon Bottou -
2016 : Jason Weston »
Jason E Weston -
2016 Workshop: Let's Discuss: Learning Methods for Dialogue »
Hal Daumé III · Paul Mineiro · Amanda Stent · Jason E Weston -
2016 : Welcome »
David Lopez-Paz · Alec Radford · Leon Bottou -
2016 Workshop: Adversarial Training »
David Lopez-Paz · Leon Bottou · Alec Radford -
2016 Poster: Dialog-based Language Learning »
Jason E Weston -
2015 Workshop: Reasoning, Attention, Memory (RAM) Workshop »
Jason E Weston · Sumit Chopra · Antoine Bordes -
2015 : Evaluating Prerequisite Qualities For End-to-End Dialog Systems »
Jason E Weston -
2015 Workshop: Optimization for Machine Learning (OPT2015) »
Suvrit Sra · Alekh Agarwal · Leon Bottou · Sashank J. Reddi -
2015 Poster: End-To-End Memory Networks »
Sainbayar Sukhbaatar · arthur szlam · Jason Weston · Rob Fergus -
2015 Poster: Learning to Segment Object Candidates »
Pedro O. Pinheiro · Ronan Collobert · Piotr Dollar -
2015 Spotlight: Learning to Segment Object Candidates »
Pedro O. Pinheiro · Ronan Collobert · Piotr Dollar -
2015 Oral: End-To-End Memory Networks »
Sainbayar Sukhbaatar · arthur szlam · Jason Weston · Rob Fergus -
2014 Workshop: 4th Workshop on Automated Knowledge Base Construction (AKBC) »
Sameer Singh · Fabian M Suchanek · Sebastian Riedel · Partha Pratim Talukdar · Kevin Murphy · Christopher Ré · William Cohen · Tom Mitchell · Andrew McCallum · Jason E Weston · Ramanathan Guha · Boyan Onyshkevych · Hoifung Poon · Oren Etzioni · Ari Kobren · Arvind Neelakantan · Peter Clark -
2014 Workshop: Learning Semantics »
Cedric Archambeau · Antoine Bordes · Leon Bottou · Chris J Burges · David Grangier -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz -
2013 Workshop: NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms »
Isabelle Guyon · Leon Bottou · Bernhard Schölkopf · Alexander Statnikov · Evelyne Viegas · james m robins -
2012 Poster: A latent factor model for highly multi-relational data »
Rodolphe Jenatton · Nicolas Le Roux · Antoine Bordes · Guillaume R Obozinski -
2011 Session: Oral Session 14 »
Ronan Collobert -
2011 Demonstration: SENNA Natural Language Processing Demo »
Ronan Collobert -
2010 Poster: Label Embedding Trees for Large Multi-Class Tasks »
Samy Bengio · Jason E Weston · David Grangier -
2009 Poster: Polynomial Semantic Indexing »
Bing Bai · Jason E Weston · David Grangier · Ronan Collobert · Kunihiko Sadamasa · Yanjun Qi · Corinna Cortes · Mehryar Mohri -
2009 Tutorial: Deep Learning in Natural Language Processing »
Ronan Collobert · Jason E Weston -
2007 Tutorial: Learning Using Many Examples »
Leon Bottou · Andrew W Moore -
2007 Poster: The Tradeoffs of Large Scale Learning »
Leon Bottou · Olivier Bousquet