基本信息
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Bio
Geoffrey Hinton designs machine learning algorithms. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets.
Research Interests
Papers共 671 篇Author StatisticsCo-AuthorSimilar Experts
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Yoshua Bengio,Geoffrey Hinton,Andrew Yao,Dawn Song,Pieter Abbeel,Trevor Darrell, Yuval Noah Harari,Ya-Qin Zhang, Lan Xue,Shai Shalev-Shwartz,Gillian Hadfield,Jeff Clune,
Science (New York, N.Y.)no. 6698 (2024): 842-845
Nature biomedical engineeringno. 6 (2023): 756-779
ICLR 2023 (2022)
ICLR 2023 (2022)
emnlp 2022pp.9751-9757, (2022)
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arXiv (Cornell University) (2022)
International Conference on Learning Representations (ICLR) (2022)
ICLR 2023 (2022)
NeurIPS 2022 (2022)
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