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We know how to spot object in images, but we must learn on more images than a human can see in a lifetime. We know how to translate text (somehow), but we must learn it on more text than a human can read in a lifetime. We know how to learn playing Atari games, but we must learn it by playing more games than any teenager can endure. The list is long. We can of course try to pin this inefficiently to some properties of our algorithms. However, we can also take the point of view that there is possibly a lot of signal in natural data that we simply do not exploit. I will report on two works in this direction. The first one establishes that something as simple as a collection of static images contains nontrivial information about the causal relations between the objects they represent. The second one, time permitting, shows how an attempt to discover such a structure in observational data led to a clear improvement of Generative Adversarial Networks.
Author Information
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
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2021 : On the Relation between Distributionally Robust Optimization and Data Curation »
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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 »
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2022 : Pre-train, fine-tune, interpolate: a three-stage strategy for domain generalization »
Alexandre Rame · Jianyu Zhang · Leon Bottou · David Lopez-Paz -
2022 Poster: The Effects of Regularization and Data Augmentation are Class Dependent »
Randall Balestriero · Leon Bottou · Yann LeCun -
2021 : Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation »
Agnieszka Słowik · Leon Bottou -
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 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 -
2016 : Welcome »
David Lopez-Paz · Alec Radford · Leon Bottou -
2016 Workshop: Adversarial Training »
David Lopez-Paz · Leon Bottou · Alec Radford -
2015 Workshop: Optimization for Machine Learning (OPT2015) »
Suvrit Sra · Alekh Agarwal · Leon Bottou · Sashank J. Reddi -
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 -
2011 Workshop: Learning Semantics »
Antoine Bordes · Jason E Weston · Ronan Collobert · Leon Bottou -
2007 Tutorial: Learning Using Many Examples »
Leon Bottou · Andrew W Moore -
2007 Poster: The Tradeoffs of Large Scale Learning »
Leon Bottou · Olivier Bousquet