D-Index & Metrics Best Publications

D-Index & Metrics 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.

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 45 Citations 12,887 120 World Ranking 4512 National Ranking 2267

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Neuroscience

David D. Cox spends much of his time researching Artificial intelligence, Cognitive neuroscience of visual object recognition, Machine learning, Facial recognition system and Pattern recognition. His study involves Deep learning, Support vector machine and Visualization, a branch of Artificial intelligence. His Cognitive neuroscience of visual object recognition research includes themes of Variation, CUDA, Communication and Computational model.

His studies in Facial recognition system integrate themes in fields like Network architecture, Pooling, Representation, Set and Stream processing. His Pattern recognition study integrates concerns from other disciplines, such as Neuromorphic engineering, Visual cortex and Test set. His work in Object incorporates the disciplines of Computer vision, Focus, Null, Wrong direction and Natural.

His most cited work include:

  • Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. (906 citations)
  • Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures (779 citations)
  • Untangling invariant object recognition. (612 citations)

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

His primary areas of investigation include Artificial intelligence, Machine learning, Cognitive neuroscience of visual object recognition, Computer vision and Pattern recognition. His Artificial neural network, Deep learning, Convolutional neural network, Facial recognition system and Representation investigations are all subjects of Artificial intelligence research. His research in Machine learning intersects with topics in Object and Set.

In his research, CUDA is intimately related to Stream processing, which falls under the overarching field of Cognitive neuroscience of visual object recognition. His work focuses on many connections between Computer vision and other disciplines, such as Computational model, that overlap with his field of interest in Null. His research investigates the connection with Pattern recognition and areas like Benchmark which intersect with concerns in Robustness.

He most often published in these fields:

  • Artificial intelligence (72.67%)
  • Machine learning (34.00%)
  • Cognitive neuroscience of visual object recognition (37.33%)

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

  • Artificial intelligence (72.67%)
  • Artificial neural network (16.67%)
  • Visual cortex (12.67%)

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

David D. Cox mostly deals with Artificial intelligence, Artificial neural network, Visual cortex, Algorithm and Convolutional neural network. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition. His study looks at the relationship between Artificial neural network and fields such as Training set, as well as how they intersect with chemical problems.

His work is dedicated to discovering how Visual cortex, Premovement neuronal activity are connected with Head, Biological neural network and Hippocampus and other disciplines. His biological study spans a wide range of topics, including Gradient descent and Mahalanobis distance. His work deals with themes such as Computational neuroscience, Pattern completion, Backward masking, Computational model and Psychophysics, which intersect with Cognitive neuroscience of visual object recognition.

Between 2017 and 2021, his most popular works were:

  • On the Information Bottleneck Theory of Deep Learning (137 citations)
  • Recurrent computations for visual pattern completion. (96 citations)
  • On the information bottleneck theory of deep learning (69 citations)

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

  • Artificial intelligence
  • Machine learning
  • Neuroscience

David D. Cox mainly investigates Artificial intelligence, Algorithm, Deep learning, Artificial neural network and Gradient descent. His work on Cognitive neuroscience of visual object recognition as part of general Artificial intelligence research is often related to Mammography, thus linking different fields of science. His work in the fields of Algorithm, such as Optimization problem, overlaps with other areas such as Momentum and Rate of convergence.

His Deep learning study combines topics in areas such as Classifier and Class imbalance. The concepts of his Artificial neural network study are interwoven with issues in Black box, Perception, Similarity, Illusion and Visual cortex. His study in Gradient descent is interdisciplinary in nature, drawing from both Function, Stochastic gradient descent and Compression.

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

Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex.

David D Cox;Robert L Savoy.
NeuroImage (2003)

1479 Citations

Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures

James Bergstra;Daniel Yamins;David Cox.
international conference on machine learning (2013)

1343 Citations

Untangling invariant object recognition.

James J. DiCarlo;David D. Cox.
Trends in Cognitive Sciences (2007)

873 Citations

Visual Place Recognition: A Survey

Stephanie Lowry;Niko Sunderhauf;Paul Newman;John J. Leonard.
IEEE Transactions on Robotics (2016)

820 Citations

Why is Real-World Visual Object Recognition Hard?

Nicolas Pinto;David Daniel Cox;David Daniel Cox;David Daniel Cox;James J DiCarlo;James J DiCarlo.
PLOS Computational Biology (2008)

726 Citations

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

William Edward Lotter;Gabriel Kreiman;David Daniel Cox.
international conference on learning representations (2016)

625 Citations

Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms

James Bergstra;Dan Yamins;David D. Cox.
Proceedings of the 12th Python in Science Conference (2013)

623 Citations

Hyperopt: a Python library for model selection and hyperparameter optimization

James Bergstra;Brent Komer;Chris Eliasmith;Dan Yamins.
Computational Science & Discovery (2015)

496 Citations

Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images

Eleonora Vig;Michael Dorr;David Cox.
computer vision and pattern recognition (2014)

415 Citations

Trade Liberalization and Industrial Organization: Some Estimates for Canada

David Cox;Richard Harris.
Journal of Political Economy (1985)

366 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing David D. Cox

Michael Milford

Michael Milford

Queensland University of Technology

Publications: 65

James J. DiCarlo

James J. DiCarlo

MIT

Publications: 62

Hans Op de Beeck

Hans Op de Beeck

Allen Institute for Brain Science

Publications: 54

Nikolaus Kriegeskorte

Nikolaus Kriegeskorte

Columbia University

Publications: 44

John-Dylan Haynes

John-Dylan Haynes

Charité - University Medicine Berlin

Publications: 41

Walter J. Scheirer

Walter J. Scheirer

University of Notre Dame

Publications: 37

Tomaso Poggio

Tomaso Poggio

MIT

Publications: 36

João Paulo Papa

João Paulo Papa

Sao Paulo State University

Publications: 30

Thomas A. Carlson

Thomas A. Carlson

University of Sydney

Publications: 29

Nancy Kanwisher

Nancy Kanwisher

MIT

Publications: 29

James V. Haxby

James V. Haxby

Dartmouth College

Publications: 26

Frank Hutter

Frank Hutter

University of Freiburg

Publications: 24

Thomas Serre

Thomas Serre

Brown University

Publications: 23

Ali Borji

Ali Borji

Verizon (United States)

Publications: 23

Chuang Gan

Chuang Gan

IBM (United States)

Publications: 22

Roland Siegwart

Roland Siegwart

ETH Zurich

Publications: 21

Trending Scientists

Helen Sharp

Helen Sharp

The Open University

Darryl J. Pappin

Darryl J. Pappin

Cold Spring Harbor Laboratory

Nuno Rodrigues Faria

Nuno Rodrigues Faria

Imperial College London

Nancy B. Schwartz

Nancy B. Schwartz

University of Chicago

Xiaolu Yang

Xiaolu Yang

University of Pennsylvania

Jian Xu

Jian Xu

Chinese Academy of Sciences

Patrick J. Blackall

Patrick J. Blackall

University of Queensland

Ronald G. Collman

Ronald G. Collman

University of Pennsylvania

Robert M. Carter

Robert M. Carter

James Cook University

Michael P. Schön

Michael P. Schön

University of Göttingen

Robert G. Maunder

Robert G. Maunder

University of Toronto

Benny Chung-ying Zee

Benny Chung-ying Zee

Chinese University of Hong Kong

Steven P. Roose

Steven P. Roose

Columbia University

Alessandro Bressan

Alessandro Bressan

International School for Advanced Studies

Julien Guy

Julien Guy

Lawrence Berkeley National Laboratory

Something went wrong. Please try again later.