Meta Chief AI Scientist Yann LeCun is blasting fellow AI luminaries for overselling AI's existential threat and asking for regulation (Ai Business, 10/2023) Why Meta’s Yann LeCun isn’t buying the AI doomer narrative (FastCompany, 05/2023)

The digit images in the MNIST set were originally selected and experimented with by Chris Burges and Corinna Cortes using bounding-box normalization and centering. Yann LeCun's version which is provided on this page uses centering by center of mass within in a larger window. Yann LeCun, Professor The Courant Institute of Mathematical Sciences

Y. LeCun and Y. Bengio. Convolutional networks for images, speech, and time-series. In M. A. Arbib, editor, The Handbook of Brain Theory and Neural Networks .

Yann LeCun, Director of AI Research, Facebook Founding Director of the NYU Center for Data Science Silver Professor of Computer Science, Neural Science, and Electrical and Computer Engineering, The Courant Institute of Mathematical Sciences, Center for Neural Science, and Electrical and Computer Engineering Department, NYU School of Engineering

Yann LeCun is Director of AI Research at Facebook and Silver Professor of Computer Science at the Courant Institute of Mathematical Sciences.

Yann LeCun, Facebook AI Research and New York University Abstract Historically, progress in neural networks and deep learning research has been greatly influenced by the available hardware and software tools. This paper identifies trends in deep learning research that will influence hardware architectures and software platforms of the future.

Yann LeCun, VP and Chief AI Scientist, Facebook Silver Professor of Computer Science, Data Science, Neural Science, and Electrical and Computer Engineering, New York University .

We propose training a convolutional neural network (LeCun et al., 1998) on pairs of small image patches where the true disparity is known (for example, obtained by LIDAR or structured light).

Much of the recreational programming I did on various computers were music related. I even wrote a program that automatically composed 2-voice counterpoint for a college project in AI.

Marc’Aurelio Ranzato Y-Lan Boureau Sumit Chopra Yann LeCun Courant Insitute of Mathematical Sciences New York University, New York, NY 10003 Abstract We introduce a view of unsupervised learn-ing that integrates probabilistic and non-probabilistic methods for clustering, dimen-sionality reduction, and feature extraction in a unified framework.