2020 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For foundational contributions to development of deep neural networks, scientific leadership in Canada, and service to the AI community.
2020 - Fellow of the Royal Society, United Kingdom
2019 - Izaak Walton Killam Memorial Prize, Canada Council
2019 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society
2018 - A. M. Turing Award For conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
2017 - Fellow of the Royal Society of Canada Academy of Science
2017 - Prix Marie-Victorin, Government of Quebec
His primary scientific interests are in Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Recurrent neural network. The concepts of his Artificial intelligence study are interwoven with issues in Natural language processing and Pattern recognition. His Machine learning research focuses on subjects like Benchmark, which are linked to Visualization, Object detection, Computer vision and Backpropagation.
His Artificial neural network study combines topics in areas such as Encoder, Representation, Feature and Machine translation. The concepts of his Recurrent neural network study are interwoven with issues in High dimensional and Hidden Markov model. The study incorporates disciplines such as Optical character recognition, Vanishing gradient problem, Convolutional Deep Belief Networks, Intelligent character recognition and Neocognitron in addition to Handwriting recognition.
Yoshua Bengio mainly investigates Artificial intelligence, Artificial neural network, Machine learning, Algorithm and Deep learning. He combines subjects such as Natural language processing, Speech recognition and Pattern recognition with his study of Artificial intelligence. As part of his studies on Speech recognition, Yoshua Bengio often connects relevant subjects like Encoder.
His Artificial neural network research incorporates elements of Generalization, Computation and Convolutional neural network. His Machine learning study incorporates themes from Inference and Benchmark. The Algorithm study combines topics in areas such as Function, Sampling and Estimator.
Artificial intelligence, Machine learning, Artificial neural network, Reinforcement learning and Deep learning are his primary areas of study. His biological study spans a wide range of topics, including Generalization and Pattern recognition. His studies in Machine learning integrate themes in fields like Adversarial system, Structure, Sample and Adaptation.
His research integrates issues of Algorithm, Computation and Robustness in his study of Artificial neural network. His Reinforcement learning research integrates issues from Context, State, Human–computer interaction and Set. His work carried out in the field of Deep learning brings together such families of science as Representation and Residual.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Deep learning, Artificial neural network and Reinforcement learning. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Encoder. Yoshua Bengio usually deals with Machine learning and limits it to topics linked to Interpolation and Decision boundary and Consistency.
Yoshua Bengio interconnects Pneumonia, Speech recognition, Cognition, Treatment efficacy and Residual in the investigation of issues within Deep learning. His work deals with themes such as Algorithm and Computation, which intersect with Artificial neural network. In his study, which falls under the umbrella issue of Reinforcement learning, Measure is strongly linked to Set.
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Yann LeCun;Yann LeCun;Yoshua Bengio;Geoffrey Hinton;Geoffrey Hinton.
Gradient-based learning applied to document recognition
Yann Lecun;Leon Bottou;Leon Bottou;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio;Patrick Haffner;Patrick Haffner.
Proceedings of the IEEE (1998)
Generative Adversarial Nets
Ian Goodfellow;Jean Pouget-Abadie;Mehdi Mirza;Bing Xu.
neural information processing systems (2014)
Ian Goodfellow;Yoshua Bengio;Aaron Courville.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot;Yoshua Bengio.
international conference on artificial intelligence and statistics (2010)
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau;Kyunghyun Cho;Yoshua Bengio.
international conference on learning representations (2015)
Learning Deep Architectures for AI
Representation Learning: A Review and New Perspectives
Y. Bengio;A. Courville;P. Vincent.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
A neural probabilistic language model
Yoshua Bengio;Réjean Ducharme;Pascal Vincent;Christian Janvin.
Journal of Machine Learning Research (2003)
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho.
international conference on machine learning (2015)
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