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Unscented Kalman filter for brain-machine interfaces

PloS one, 2009-07, Vol.4 (7), p.e6243-e6243

2009

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Efficient BackProp

Neural Networks: Tricks of the Trade, p.9-48

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Image and video coding-emerging standards and beyond

IEEE transactions on circuits and systems for video technology, 1998-11, Vol.8 (7), p.814-837

1998

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Neural Networks: Tricks of the Trade

2nd Edition, 2012

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Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation

Neural Networks: Tricks of the Trade, p.235-269

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Transformation Invariance in Pattern Recognition — Tangent Distance and Tangent Propagation

Neural Networks: Tricks of the Trade, p.239-274

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Image and Video Coding—Emerging Standards and Beyond

Readings in Multimedia Computing and Networking, p.89-112

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Learning Hierarchical Features for Scene Labeling

IEEE transactions on pattern analysis and machine intelligence, 2013-08, Vol.35 (8), p.1915-1929

2013

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Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks

ACM transactions on graphics, 2014-08, Vol.33 (5), p.1-10

2014

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The Power and Limits of Deep Learning: In his IRI Medal address, Yann LeCun maps the development of machine learning techniques and suggests what the future may hold

Research technology management, 2018-11, Vol.61 (6), p.22-27

LeCun, Yann

2018

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Language, common sense, and the Winograd schema challenge

Artificial intelligence, 2023-12, Vol.325, p.104031, Article 104031

2023

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Will Learning Machines Take Over the World?

Frontiers for young minds, 2024-05, Vol.12

LeCun, Yann

2024

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MDETR - Modulated Detection for End-to-End Multi-Modal Understanding

2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, p.1760-1770

2021

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A Closer Look at Spatiotemporal Convolutions for Action Recognition

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.6450-6459

2018

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Deep learning, reinforcement learning, and world models

Neural networks, 2022-08, Vol.152, p.267-275

2022

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Predicting Deeper into the Future of Semantic Segmentation

2017 IEEE International Conference on Computer Vision (ICCV), 2017, p.648-657

2017

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Computing the stereo matching cost with a convolutional neural network

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, p.1592-1599

2015

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Very deep multilingual convolutional neural networks for LVCSR

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, p.4955-4959

2016

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Efficient object localization using Convolutional Networks

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, p.648-656

2015

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Statistical Machine Learning and Dissolved Gas Analysis: A Review

IEEE transactions on power delivery, 2012-10, Vol.27 (4), p.1791-1799

2012

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A hierarchical loss and its problems when classifying non-hierarchically

PloS one, 2019-12, Vol.14 (12), p.e0226222-e0226222

2019

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The Power and Limits of Deep Learning

Research technology management, 2018-01, Vol.61 (6), p.22-27

LeCun, Yann

2018

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1.1 Deep Learning Hardware: Past, Present, and Future

2019 IEEE International Solid- State Circuits Conference - (ISSCC), 2019, p.12-19

LeCun, Yann

2019

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Catalyzing next-generation Artificial Intelligence through NeuroAI

Nature communications, 2023-03, Vol.14 (1), p.1597-7, Article 1597

2023

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Convolutional networks and applications in vision

2010 IEEE International Symposium on Circuits and Systems (ISCAS), 2010, p.253-256

2010

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Unscented Kalman filter for brain-machine interfaces

Efficient BackProp

Image and video coding-emerging standards and beyond

Neural Networks: Tricks of the Trade

Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation

Transformation Invariance in Pattern Recognition — Tangent Distance and Tangent Propagation

Image and Video Coding—Emerging Standards and Beyond

Learning Hierarchical Features for Scene Labeling

Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks

The Power and Limits of Deep Learning: In his IRI Medal address, Yann LeCun maps the development of machine learning techniques and suggests what the future may hold

Language, common sense, and the Winograd schema challenge

Will Learning Machines Take Over the World?

MDETR - Modulated Detection for End-to-End Multi-Modal Understanding

A Closer Look at Spatiotemporal Convolutions for Action Recognition

Deep learning, reinforcement learning, and world models

Predicting Deeper into the Future of Semantic Segmentation

Computing the stereo matching cost with a convolutional neural network

Very deep multilingual convolutional neural networks for LVCSR

Efficient object localization using Convolutional Networks

Statistical Machine Learning and Dissolved Gas Analysis: A Review

A hierarchical loss and its problems when classifying non-hierarchically

The Power and Limits of Deep Learning

1.1 Deep Learning Hardware: Past, Present, and Future

Catalyzing next-generation Artificial Intelligence through NeuroAI

Convolutional networks and applications in vision

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