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Author Information
Randall Balestriero (Meta AI, FAIR)
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 .
Yann LeCun (Facebook)
Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University. He received the Electrical Engineer Diploma from ESIEE, Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits for computer perception.
More from the Same Authors
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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 : Exact Visualization of Deep Neural Network Geometry and Decision Boundary »
Ahmed Imtiaz Humayun · Randall Balestriero · Richard Baraniuk -
2022 : Pre-train, fine-tune, interpolate: a three-stage strategy for domain generalization »
Alexandre Rame · Jianyu Zhang · Leon Bottou · David Lopez-Paz -
2023 Poster: Understanding the detrimental class-level effects of data augmentation »
Polina Kirichenko · Mark Ibrahim · Randall Balestriero · Diane Bouchacourt · Shanmukha Ramakrishna Vedantam · Hamed Firooz · Andrew Wilson -
2023 Poster: Self-Supervised Learning with Lie Symmetries for Partial Differential Equations »
Grégoire Mialon · Quentin Garrido · Hannah Lawrence · Danyal Rehman · Bobak Kiani · Yann LeCun -
2023 Poster: Reverse Engineering Self-Supervised Learning »
Ido Ben-Shaul · Ravid Shwartz-Ziv · Tomer Galanti · Shai Dekel · Yann LeCun -
2023 Poster: An Information Theory Perspective on Variance-Invariance-Covariance Regularization »
Ravid Shwartz-Ziv · Randall Balestriero · Kenji Kawaguchi · Tim G. J. Rudner · Yann LeCun -
2023 Poster: Birth of a Transformer: A Memory Viewpoint »
Alberto Bietti · Vivien Cabannes · Diane Bouchacourt · Herve Jegou · Leon Bottou -
2022 Poster: VICRegL: Self-Supervised Learning of Local Visual Features »
Adrien Bardes · Jean Ponce · Yann LeCun -
2022 Poster: Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone »
Zi-Yi Dou · Aishwarya Kamath · Zhe Gan · Pengchuan Zhang · Jianfeng Wang · Linjie Li · Zicheng Liu · Ce Liu · Yann LeCun · Nanyun Peng · Jianfeng Gao · Lijuan Wang -
2022 Poster: Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors »
Ravid Shwartz-Ziv · Micah Goldblum · Hossein Souri · Sanyam Kapoor · Chen Zhu · Yann LeCun · Andrew Wilson -
2022 Poster: A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training »
Randall Balestriero · Ishan Misra · Yann LeCun -
2022 Poster: projUNN: efficient method for training deep networks with unitary matrices »
Bobak Kiani · Randall Balestriero · Yann LeCun · Seth Lloyd -
2022 Poster: Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods »
Randall Balestriero · Yann LeCun -
2021 : Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation »
Agnieszka Słowik · Leon Bottou -
2020 : Panel Discussion & Closing »
Yejin Choi · Alexei Efros · Chelsea Finn · Kristen Grauman · Quoc V Le · Yann LeCun · Ruslan Salakhutdinov · Eric Xing -
2020 : QA: Yann LeCun »
Yann LeCun -
2020 : Invited Talk: Yann LeCun »
Yann LeCun -
2020 Poster: Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks »
Randall Balestriero · Sebastien PARIS · Richard Baraniuk -
2019 : TBD »
Yann LeCun -
2019 Poster: Cold Case: The Lost MNIST Digits »
Chhavi Yadav · Leon Bottou -
2019 Spotlight: Cold Case: The Lost MNIST Digits »
Chhavi Yadav · Leon Bottou -
2019 Poster: The Geometry of Deep Networks: Power Diagram Subdivision »
Randall Balestriero · Romain Cosentino · Behnaam Aazhang · Richard Baraniuk -
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 : Panel Session »
Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther -
2017 : Geometrical Insights for Unsupervised Learning »
Leon Bottou -
2017 : Looking for a Missing Signal »
Leon Bottou -
2017 Tutorial: Geometric Deep Learning on Graphs and Manifolds »
Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun -
2016 : Discussion panel »
Ian Goodfellow · Soumith Chintala · Arthur Gretton · Sebastian Nowozin · Aaron Courville · Yann LeCun · Emily Denton -
2016 : Energy-Based Adversarial Training and Video Prediction »
Yann LeCun -
2016 : Welcome »
David Lopez-Paz · Alec Radford · Leon Bottou -
2016 Workshop: Adversarial Training »
David Lopez-Paz · Leon Bottou · Alec Radford -
2016 Symposium: Deep Learning Symposium »
Yoshua Bengio · Yann LeCun · Navdeep Jaitly · Roger Grosse -
2015 Workshop: Optimization for Machine Learning (OPT2015) »
Suvrit Sra · Alekh Agarwal · Leon Bottou · Sashank J. Reddi -
2015 Poster: Learning to Linearize Under Uncertainty »
Ross Goroshin · Michael Mathieu · Yann LeCun -
2015 Poster: Character-level Convolutional Networks for Text Classification »
Xiang Zhang · Junbo (Jake) Zhao · Yann LeCun -
2015 Poster: Deep learning with Elastic Averaging SGD »
Sixin Zhang · Anna Choromanska · Yann LeCun -
2015 Spotlight: Deep learning with Elastic Averaging SGD »
Sixin Zhang · Anna Choromanska · Yann LeCun -
2015 Tutorial: Deep Learning »
Geoffrey E Hinton · Yoshua Bengio · Yann LeCun -
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