Yann LeCun

CHIEF AI SCIENTIST | NEW YORK CITY, UNITED STATES

Yann is Chief AI Scientist for Facebook AI Research (FAIR), joining Facebook in December 2013. He is also a Silver Professor at New York University on a part time basis, mainly affiliated with the NYU Center for Data Science, and the Courant Institute of Mathematical Sciences.

He received the EE Diploma from Ecole Supérieure d’Ingénieurs en Electrotechnique et Electronique (ESIEE Paris), and a PhD in CS 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 was the founding director of the NYU Center for Data Science. Yann is the co-director of the CIFAR program on Neural Computation and Adaptive Perception Program with Yoshua Bengio.

He is a member of the US National Academy of Engineering, a Chevalier de la Légion d’Honneur, a fellow of AAAI, the recipient of the 2014 IEEE Neural Network Pioneer Award, the 2015 IEEE Pattern Analysis and Machine Intelligence Distinguished Researcher Award, the 2016 Lovie Award for Lifetime Achievement, the University of Pennsylvania Pender Award, and received honorary doctorates from IPN, Mexico and EPFL.

He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing."

His research interests include machine learning and artificial intelligence, with applications to computer vision, natural language understanding, robotics, and computational neuroscience. He is best known for his work in deep learning and the invention of the convolutional network method which is widely used for image, video and speech recognition.

Yann's Publications

July 24, 2024

COMPUTER VISION

X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs

Vlad Sobal, Mark Ibrahim, Randall Balestriero, Vivien Cabannes, Pietro Astolfi, Kyunghyun Cho, Yann LeCun

July 24, 2024

May 06, 2024

CONVERSATIONAL AI

NLP

GAIA: a benchmark for general AI assistants

Gregoire Mialon, Yann LeCun, Thomas Scialom, Clémentine Fourrier, Thomas Wolf

May 06, 2024

February 15, 2024

CORE MACHINE LEARNING

Revisiting Feature Prediction for Learning Visual Representations from Video

Adrien Bardes, Quentin Garrido, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, Nicolas Ballas, Jean Ponce

February 15, 2024

October 05, 2023

CORE MACHINE LEARNING

Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need

Vivien Cabannes, Leon Bottou, Yann LeCun, Randall Balestriero

October 05, 2023

July 06, 2023

HUMAN & MACHINE INTELLIGENCE

REINFORCEMENT LEARNING

Augmented Language Models: a Survey

Gregoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, Thomas Scialom

July 06, 2023

June 27, 2023

CORE MACHINE LEARNING

Self-Supervised Learning of Split Invariant Equivariant Representations

Quentin Garrido, Laurent Najman, Yann LeCun

June 27, 2023

June 26, 2023

CORE MACHINE LEARNING

The SSL Interplay: Augmentations, Inductive Bias, and Generalization

Vivien Cabannes, Bobak Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti

June 26, 2023

June 18, 2023

CORE MACHINE LEARNING

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Mido Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Mike Rabbat, Yann LeCun, Nicolas Ballas

June 18, 2023

February 28, 2023

CORE MACHINE LEARNING

On the duality between contrastive and non-contrastive self-supervised learning

Quentin Garrido, Adrien Bardes, Yann LeCun, Yubei Chen, Laurent Najman

February 28, 2023

October 28, 2022

COMPUTER VISION

CORE MACHINE LEARNING

Decoupled Contrastive Learning

Yubei Chen, Yann LeCun

October 28, 2022

October 04, 2022

RESEARCH

ML APPLICATIONS

VICRegL: Self-Supervised Learning of Local Visual Features

Adrien Bardes, Yann LeCun, Jean Ponce

October 04, 2022

October 12, 2021

Inspirational Adversarial Image Generation

Baptiste Rozière, Camille Couprie, Jérémy Rapin, Morgane Rivière, Olivier Teytaud, Yann LeCun

October 12, 2021

October 22, 2020

RESEARCH

SPEECH & AUDIO

Implicit Rank-Minimizing Autoencoder

Li Jing, Jure Zbontar, Yann LeCun

October 22, 2020

July 01, 2020

RESEARCH

A hierarchical loss and its problems when classifying non-hierarchically

Mark Tygert, Cinna Wu, Yann LeCun

July 01, 2020

May 15, 2019

GLoMo: Unsupervisedly Learned Relational tGraphs as Transferable Representations

Jake Zhao, Kaiming He, Yann LeCun, Bhuwan Dhingra, Ruslan Salakhutdinov, William Cohen, Zhilin Yang

May 15, 2019

September 10, 2018

RESEARCH

Predicting Future Instance Segmentation by Forecasting Convolutional Features

Pauline Luc, Camille Couprie, Yann LeCun, Jakob Verbeek

September 10, 2018