2023 - Research.com Computer Science in United States Leader Award
2020 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to deep learning, neural networks, and image recognition, including the introduction of convolutional neural networks.
2018 - A. M. Turing Award For conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
2017 - Member of the National Academy of Engineering For developing convolutional neural networks and their applications in computer vision and other areas of artificial intelligence.
2014 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society
Yann LeCun mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Computer vision. Many of his studies involve connections with topics such as Speech recognition and Artificial intelligence. His Speech recognition study integrates concerns from other disciplines, such as Backpropagation and Digit recognition.
The various areas that Yann LeCun examines in his Backpropagation study include Representation and Computational model. His Pattern recognition research incorporates themes from Optical flow and Word error rate. His work deals with themes such as Bag-of-words model, Natural language processing, Geometric data analysis and Character, which intersect with Deep learning.
Yann LeCun spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Computer vision. Artificial intelligence and Speech recognition are frequently intertwined in his study. Yann LeCun combines subjects such as Pixel, Invariant and Pooling with his study of Pattern recognition.
His Computer vision study incorporates themes from Classifier and Robot. In his study, which falls under the umbrella issue of Feature extraction, Object detection is strongly linked to Cognitive neuroscience of visual object recognition. Yann LeCun regularly links together related areas like Algorithm in his Deep learning studies.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Algorithm, Artificial neural network and Deep learning. His research links Pattern recognition with Artificial intelligence. In general Machine learning study, his work on Latent variable often relates to the realm of Space, thereby connecting several areas of interest.
He has researched Algorithm in several fields, including Redundancy, Function, Representation and Generator. The study incorporates disciplines such as Generalization, Mathematical optimization and Nested loop join in addition to Artificial neural network. His work on MNIST database as part of general Deep learning study is frequently linked to Spin glass, therefore connecting diverse disciplines of science.
Yann LeCun mainly investigates Artificial intelligence, Artificial neural network, Machine learning, Deep learning and Convolutional neural network. His Artificial intelligence research includes elements of Generator, Theoretical computer science and Pattern recognition. His Pattern recognition research includes themes of Object detection and Block.
His biological study spans a wide range of topics, including Generalization, Mathematical optimization and Maxima and minima. His study in Deep learning is interdisciplinary in nature, drawing from both Semi-supervised learning, Computer hardware, Discriminator, Function and Software. His Convolutional neural network research is multidisciplinary, incorporating perspectives in Optical flow, Segmentation and RGB color model.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Yann LeCun;Yann LeCun;Yoshua Bengio;Geoffrey Hinton;Geoffrey Hinton.
Gradient-based learning applied to document recognition
Y. Lecun;L. Bottou;L. Bottou;Y. Bengio;Y. Bengio;Y. Bengio;P. Haffner.
Proceedings of the IEEE (1998)
Backpropagation applied to handwritten zip code recognition
Y. LeCun;B. Boser;J. S. Denker;D. Henderson.
Neural Computation (1989)
Convolutional networks for images, speech, and time series
Yann LeCun;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio.
The handbook of brain theory and neural networks (1998)
Yann LeCun;Léon Bottou;Genevieve B. Orr;Klaus-Robert Müller.
neural information processing systems (1998)
Handwritten Digit Recognition with a Back-Propagation Network
Yann LeCun;Bernhard E. Boser;John S. Denker;John S. Denker;Donnie Henderson.
neural information processing systems (1989)
Optimal Brain Damage
Yann LeCun;John S. Denker;Sara A. Solla.
neural information processing systems (1989)
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Pierre Sermanet;David Eigen;Xiang Zhang;Michael Mathieu.
international conference on learning representations (2014)
Character-level convolutional networks for text classification
Xiang Zhang;Junbo Zhao;Yann LeCun.
neural information processing systems (2015)
Learning a similarity metric discriminatively, with application to face verification
S. Chopra;R. Hadsell;Y. LeCun.
computer vision and pattern recognition (2005)
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