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 48 Citations 10,020 150 World Ranking 3098 National Ranking 1627

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Michael C. Mozer mainly focuses on Artificial intelligence, Cognitive psychology, Perception, Machine learning and Artificial neural network. His Artificial intelligence research incorporates themes from Heuristics and Pattern recognition. The Cognitive psychology study combines topics in areas such as Word recognition, Dyslexia, Reading, Stimulus and Morpheme.

His study in the fields of Visual perception and Object-based attention under the domain of Perception overlaps with other disciplines such as Object. His Visual perception course of study focuses on Communication and Speech recognition. His biological study spans a wide range of topics, including Automation, Architectural engineering, State and Operations management.

His most cited work include:

  • Bayesian community-wide culture-independent microbial source tracking (641 citations)
  • The Neural Network House: An Environment that Adapts to its Inhabitants (503 citations)
  • Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment (444 citations)

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

His primary areas of investigation include Artificial intelligence, Cognitive psychology, Artificial neural network, Machine learning and Pattern recognition. His Artificial intelligence research integrates issues from Cognition and Perception. Cognition is closely attributed to Cognitive science in his research.

His research in Perception is mostly focused on Visual perception. In the field of Cognitive psychology, his study on Visual search overlaps with subjects such as Perspective. His work focuses on many connections between Artificial neural network and other disciplines, such as Reinforcement learning, that overlap with his field of interest in Context.

He most often published in these fields:

  • Artificial intelligence (56.97%)
  • Cognitive psychology (21.72%)
  • Artificial neural network (19.26%)

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

  • Artificial intelligence (56.97%)
  • Machine learning (18.85%)
  • Artificial neural network (19.26%)

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

The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Artificial neural network, Pattern recognition and Embedding. Michael C. Mozer performs integrative study on Artificial intelligence and Generalization. His research in Machine learning intersects with topics in Multi-task learning and Human–machine system.

In his study, Histogram, Transfer of learning and Confidence interval is strongly linked to Metric, which falls under the umbrella field of Artificial neural network. His work carried out in the field of Pattern recognition brings together such families of science as Perceptual similarity, Perception, Dilation, Linear map and Image. His study in Embedding is interdisciplinary in nature, drawing from both Theoretical computer science, Representation, Class, Function and Supervised learning.

Between 2016 and 2021, his most popular works were:

  • Deep neural network improves fracture detection by clinicians. (146 citations)
  • Learning Deep Disentangled Embeddings With the F-Statistic Loss (77 citations)
  • Learning to generate images with perceptual similarity metrics (70 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, Machine learning, Embedding, Theoretical computer science and Recurrent neural network. His Artificial intelligence study combines topics in areas such as Memorization, Perception and Pattern recognition. His work on Artificial neural network and Feature learning as part of general Machine learning study is frequently linked to Consistency, therefore connecting diverse disciplines of science.

His Embedding research is multidisciplinary, incorporating perspectives in Selection algorithm, Structure, Function and Cognitive model. His Theoretical computer science study incorporates themes from Gradient descent, Algorithm design and Data collection. In his work, Algorithm, Reinforcement learning, Information flow and Context is strongly intertwined with Robustness, which is a subfield of Recurrent neural network.

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

The Neural Network House: An Environment that Adapts to its Inhabitants

Michael C. Mozer.
(1998)

782 Citations

Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment

Michael C. Mozer;Paul Smolensky.
neural information processing systems (1988)

759 Citations

Bayesian community-wide culture-independent microbial source tracking

Dan Knights;Justin Kuczynski;Emily S. Charlson;Jesse Zaneveld.
Nature Methods (2011)

749 Citations

Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry

M.C. Mozer;R. Wolniewicz;D.B. Grimes;E. Johnson.
IEEE Transactions on Neural Networks (2000)

458 Citations

Neural net architectures for temporal sequence processing

Michael C. Mozer.
(2007)

439 Citations

An Intelligent Environment Must Be Adaptive

M.C. Mozer.
IEEE Intelligent Systems & Their Applications (1999)

390 Citations

Using Relevance to Reduce Network Size Automatically

Michael C. Mozer;Paul Smolensky.
Connection Science (1989)

363 Citations

The Perception of Multiple Objects: A Connectionist Approach

Michael C. Mozer.
(1991)

324 Citations

Optimizing distributed practice: theoretical analysis and practical implications.

Nicholas J Cepeda;Noriko Coburn;Doug Rohrer;John T Wixted.
Experimental Psychology (2009)

323 Citations

Optimizing classifier performance via an approximation to the Wilcoxon-Mann-Whitney statistic

Lian Yan;Robert Dodier;Michael C. Mozer;Richard Wolniewicz.
international conference on machine learning (2003)

322 Citations

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