His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Training set and Support vector machine. Léon Bottou regularly ties together related areas like Algorithm in his Artificial intelligence studies. Léon Bottou studies Pattern recognition, focusing on Handwriting recognition in particular.
His research integrates issues of Neocognitron, Vanishing gradient problem, Optical character recognition and Intelligent character recognition in his study of Handwriting recognition. In his research on the topic of Training set, Tag system is strongly related with Task. He has researched Support vector machine in several fields, including Scalability, Scalable algorithms and Data mining.
Léon Bottou mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Algorithm and Support vector machine. His research on Artificial intelligence often connects related areas such as Computer vision. Many of his studies on Machine learning involve topics that are commonly interrelated, such as Training set.
Léon Bottou works in the field of Pattern recognition, focusing on Handwriting recognition in particular. In his study, Stochastic gradient descent is strongly linked to Mathematical optimization, which falls under the umbrella field of Algorithm. His Convolutional neural network study combines topics in areas such as Optical character recognition and Transformer.
Léon Bottou mainly focuses on Artificial intelligence, Artificial neural network, Theoretical computer science, Deep learning and Applied mathematics. His Artificial intelligence study incorporates themes from Machine learning, Optimization problem and Pattern recognition. His work on Recurrent neural network as part of general Artificial neural network research is often related to Initialization, Hamiltonian mechanics and Dynamical systems theory, thus linking different fields of science.
The Theoretical computer science study combines topics in areas such as Embedding, Cluster analysis and Visualization. His study focuses on the intersection of Deep learning and fields such as Spectrogram with connections in the field of Generator. His Applied mathematics research incorporates themes from Stochastic gradient descent, Parametric statistics and Lipschitz continuity.
His scientific interests lie mostly in Artificial intelligence, Deep learning, Stochastic gradient descent, Lipschitz continuity and Artificial neural network. The study incorporates disciplines such as Optimization problem, Machine learning, Invariant and Pattern recognition in addition to Artificial intelligence. His Machine learning study focuses on Deep neural networks in particular.
His Deep learning research is multidisciplinary, relying on both Gradient descent and Normalization. His Stochastic gradient descent research is multidisciplinary, incorporating elements of Stationary point, Robustness and Applied mathematics. His work on Recurrent neural network as part of general Artificial neural network study is frequently connected to Hamiltonian mechanics, Dynamical systems theory and Symplectic integrator, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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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)
Wasserstein Generative Adversarial Networks
Martin Arjovsky;Soumith Chintala;Léon Bottou.
international conference on machine learning (2017)
Natural Language Processing (Almost) from Scratch
Ronan Collobert;Jason Weston;Léon Bottou;Michael Karlen.
Journal of Machine Learning Research (2011)
Large-Scale Machine Learning with Stochastic Gradient Descent
Yann LeCun;Léon Bottou;Genevieve B. Orr;Klaus-Robert Müller.
neural information processing systems (1998)
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Maxime Oquab;Maxime Oquab;Leon Bottou;Ivan Laptev;Josef Sivic.
computer vision and pattern recognition (2014)
SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK
Jane Bromley;James W. Bentz;James W. Bentz;Léon Bottou;Léon Bottou;Isabelle Guyon.
International Journal of Pattern Recognition and Artificial Intelligence (1993)
Optimization Methods for Large-Scale Machine Learning
Léon Bottou;Frank E. Curtis;Jorge Nocedal.
Siam Review (2018)
Stochastic Gradient Descent Tricks
Neural Networks: Tricks of the Trade (2nd ed.) (2012)
The Tradeoffs of Large Scale Learning
Olivier Bousquet;Léon Bottou.
neural information processing systems (2007)
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