D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 35,950 42 World Ranking 8654 National Ranking 4041

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

George E. Dahl spends much of his time researching Artificial neural network, Artificial intelligence, Hidden Markov model, Mixture model and Speech recognition. George E. Dahl interconnects Ground truth, Message passing and Benchmark in the investigation of issues within Artificial neural network. His Artificial intelligence study integrates concerns from other disciplines, such as Quantitative structure and Machine learning.

The Hidden layer research George E. Dahl does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Training, Curvature, Momentum and Schedule, therefore creating a link between diverse domains of science. His Hidden Markov model research includes themes of Time delay neural network and Margin. His Pattern recognition research incorporates themes from Deep belief network and Dropout.

His most cited work include:

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups (6052 citations)
  • On the importance of initialization and momentum in deep learning (2450 citations)
  • Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition (2360 citations)

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

His primary scientific interests are in Artificial intelligence, Artificial neural network, Machine learning, Speech recognition and Pattern recognition. His study in the field of Deep learning, Language model and Sentence is also linked to topics like Vocabulary. His Artificial neural network research is multidisciplinary, incorporating perspectives in Pipeline and Speedup.

His work on Hidden Markov model and Word error rate as part of general Speech recognition study is frequently linked to FMLLR, bridging the gap between disciplines. His work deals with themes such as Mixture model, Deep belief network and Discriminative model, which intersect with Hidden Markov model. His studies deal with areas such as Time delay neural network and Margin as well as Mixture model.

He most often published in these fields:

  • Artificial intelligence (66.67%)
  • Artificial neural network (48.15%)
  • Machine learning (29.63%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (66.67%)
  • Artificial neural network (48.15%)
  • Machine learning (29.63%)

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

George E. Dahl focuses on Artificial intelligence, Artificial neural network, Machine learning, Deep learning and Language model. He integrates several fields in his works, including Artificial intelligence, Nature versus nurture, Sentinel lymph node, Nodal metastasis, Metastatic breast cancer and Lymph node. His research in Artificial neural network intersects with topics in Pipeline and Speedup.

His Machine learning study typically links adjacent topics like Training set. The study incorporates disciplines such as Normalization and Human intelligence in addition to Deep learning. His Language model research includes elements of Sentence, Automatic summarization and Machine translation.

Between 2017 and 2021, his most popular works were:

  • Relational inductive biases, deep learning, and graph networks (891 citations)
  • Motivating the Rules of the Game for Adversarial Example Research (124 citations)
  • Measuring the Effects of Data Parallelism on Neural Network Training (118 citations)

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

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

Deep Neural Networks for Acoustic Modeling in Speech Recognition

Geoffrey Hinton;Li Deng;Dong Yu;George Dahl.
IEEE Signal Processing Magazine (2012)

5940 Citations

On the importance of initialization and momentum in deep learning

Ilya Sutskever;James Martens;George Dahl;Geoffrey Hinton.
international conference on machine learning (2013)

2957 Citations

Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

G. E. Dahl;Dong Yu;Li Deng;A. Acero.
IEEE Transactions on Audio, Speech, and Language Processing (2012)

2923 Citations

Acoustic Modeling Using Deep Belief Networks

A. Mohamed;G. E. Dahl;G. Hinton.
IEEE Transactions on Audio, Speech, and Language Processing (2012)

1743 Citations

Improving deep neural networks for LVCSR using rectified linear units and dropout

George E. Dahl;Tara N. Sainath;Geoffrey E. Hinton.
international conference on acoustics, speech, and signal processing (2013)

1354 Citations

Neural Message Passing for Quantum Chemistry

Justin Gilmer;Samuel S. Schoenholz;Patrick F. Riley;Oriol Vinyals.
international conference on machine learning (2017)

988 Citations

Relational inductive biases, deep learning, and graph networks

Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez.
arXiv: Learning (2018)

873 Citations

Deep neural nets as a method for quantitative structure-activity relationships.

Junshui Ma;Robert P. Sheridan;Andy Liaw;George E. Dahl.
Journal of Chemical Information and Modeling (2015)

804 Citations

Deep Convolutional Neural Networks for Large-scale Speech Tasks

Tara N. Sainath;Brian Kingsbury;George Saon;Hagen Soltau.
Neural Networks (2015)

428 Citations

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Best Scientists Citing George E. Dahl

Li Deng

Li Deng

Citadel

Publications: 116

Dong Yu

Dong Yu

Tencent (China)

Publications: 109

Yoshua Bengio

Yoshua Bengio

University of Montreal

Publications: 106

Jinyu Li

Jinyu Li

Microsoft (United States)

Publications: 90

Yifan Gong

Yifan Gong

Microsoft (United States)

Publications: 90

Klaus-Robert Müller

Klaus-Robert Müller

Technical University of Berlin

Publications: 72

Tara N. Sainath

Tara N. Sainath

Google (United States)

Publications: 57

Shinji Watanabe

Shinji Watanabe

Carnegie Mellon University

Publications: 54

Chin-Hui Lee

Chin-Hui Lee

Georgia Institute of Technology

Publications: 54

Björn Schuller

Björn Schuller

Imperial College London

Publications: 46

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 45

Jun Du

Jun Du

University of Science and Technology of China

Publications: 43

Mark J. F. Gales

Mark J. F. Gales

University of Cambridge

Publications: 43

Haizhou Li

Haizhou Li

Chinese University of Hong Kong, Shenzhen

Publications: 43

Kyunghyun Cho

Kyunghyun Cho

New York University

Publications: 43

Steve Renals

Steve Renals

University of Edinburgh

Publications: 41

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