H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 151 Citations 608,621 327 World Ranking 11 National Ranking 2

Research.com Recognitions

Awards & Achievements

2018 - A. M. Turing Award For conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

2016 - IEEE/RSE Wolfson James Clerk Maxwell Medal “For pioneering and sustained contributions to machine learning, including developments in deep neural networks.”

2016 - Member of the National Academy of Engineering For contributions to the theory and practice of artificial neural networks and their application to speech recognition and computer vision.

2014 - IEEE Frank Rosenblatt Award

2003 - Fellow of the American Academy of Arts and Sciences

1998 - Fellow of the Royal Society, United Kingdom

1996 - Fellow of the Royal Society of Canada Academy of Science

1990 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI)

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

His primary areas of investigation include Artificial intelligence, Artificial neural network, Machine learning, Pattern recognition and Speech recognition. Artificial intelligence and Layer are two areas of study in which he engages in interdisciplinary research. His Artificial neural network research is multidisciplinary, incorporating elements of Dropout, Task, Mixture model, Convolutional neural network and Generative model.

His work in Convolutional neural network tackles topics such as Regularization which are related to areas like Similarity learning, Vanishing gradient problem, Feature learning and Fisher vector. His biological study spans a wide range of topics, including Variable-order Bayesian network, Bayesian statistics, Sigmoid function and Frequentist inference. His research in Speech recognition focuses on subjects like Time delay neural network, which are connected to Frame and Margin.

His most cited work include:

  • ImageNet Classification with Deep Convolutional Neural Networks (42297 citations)
  • Deep learning (28315 citations)
  • Deep learning (28315 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Algorithm. His Artificial intelligence study frequently involves adjacent topics like Computer vision. His Artificial neural network study combines topics in areas such as Speech recognition and Generalization.

His studies deal with areas such as Image and Deep belief network as well as Pattern recognition. His Machine learning research is multidisciplinary, incorporating perspectives in Structure, Probabilistic logic and Inference. His Algorithm study combines topics from a wide range of disciplines, such as Latent variable, Simple, Theoretical computer science and Mathematical optimization.

He most often published in these fields:

  • Artificial intelligence (66.02%)
  • Artificial neural network (33.01%)
  • Pattern recognition (24.27%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (66.02%)
  • Artificial neural network (33.01%)
  • Pattern recognition (24.27%)

In recent papers he was focusing on the following fields of study:

Geoffrey E. Hinton focuses on Artificial intelligence, Artificial neural network, Pattern recognition, Machine learning and Algorithm. His Image, Representation, MNIST database, Deep learning and Class study are his primary interests in Artificial intelligence. His Deep learning research incorporates elements of Backpropagation and Scalability.

His research in Artificial neural network intersects with topics in Language model, Salient, Cortex and Decision tree. The concepts of his Pattern recognition study are interwoven with issues in Smoothing, Data point and Contextual image classification. Geoffrey E. Hinton combines subjects such as Simple and Fraction with his study of Machine learning.

Between 2016 and 2021, his most popular works were:

  • ImageNet classification with deep convolutional neural networks (6965 citations)
  • Dynamic Routing Between Capsules (1796 citations)
  • A Simple Framework for Contrastive Learning of Visual Representations (944 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of study are Artificial intelligence, Artificial neural network, Machine learning, Pattern recognition and Hyperparameter. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Key. His study in the field of Backpropagation is also linked to topics like Layer.

The various areas that Geoffrey E. Hinton examines in his Machine learning study include Test and Training set. His work on Convolutional neural network as part of general Pattern recognition study is frequently linked to Test data, therefore connecting diverse disciplines of science. His Deep learning research is multidisciplinary, relying on both Routing and Differential.

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.

Best Publications

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky;Ilya Sutskever;Geoffrey E. Hinton.
neural information processing systems (2012)

70201 Citations

Deep learning

Yann LeCun;Yann LeCun;Yoshua Bengio;Geoffrey Hinton;Geoffrey Hinton.
Nature (2015)

30518 Citations

Learning internal representations by error propagation

D. E. Rumelhart;G. E. Hinton;R. J. Williams.
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1 (1986)

27513 Citations

Learning representations by back-propagating errors

David E. Rumelhart;Geoffrey E. Hinton;Ronald J. Williams.
Nature (1988)

23677 Citations

Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava;Geoffrey Hinton;Alex Krizhevsky;Ilya Sutskever.
Journal of Machine Learning Research (2014)

22730 Citations

Visualizing Data using t-SNE

Laurens van der Maaten;Geoffrey Hinton.
Journal of Machine Learning Research (2008)

15602 Citations

A fast learning algorithm for deep belief nets

Geoffrey E. Hinton;Simon Osindero;Yee-Whye Teh.
Neural Computation (2006)

13985 Citations

Reducing the Dimensionality of Data with Neural Networks

G. E. Hinton;R. R. Salakhutdinov.
Science (2006)

13175 Citations

Rectified Linear Units Improve Restricted Boltzmann Machines

Vinod Nair;Geoffrey E. Hinton.
international conference on machine learning (2010)

10754 Citations

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

G. Hinton;Li Deng;Dong Yu;G. E. Dahl.
IEEE Signal Processing Magazine (2012)

8695 Citations

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