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 31 Citations 10,355 86 World Ranking 7596 National Ranking 3565

Research.com Recognitions

Awards & Achievements

2009 - ACM Fellow For the development of dynamic Bayes networks and anytime algorithms.

1994 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For contributions to the fields of automated planning, temporal reasoning, and robotics.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Programming language

The scientist’s investigation covers issues in Mathematical optimization, Markov decision process, Artificial intelligence, Partially observable Markov decision process and Function. His Mathematical optimization study incorporates themes from Domain, Motion planning and Mobile robot. His Markov decision process study typically links adjacent topics like Heuristic.

His research on Artificial intelligence frequently links to adjacent areas such as Temporal logic. The various areas that he examines in his Partially observable Markov decision process study include Automaton and Minification. As a part of the same scientific study, he usually deals with the Management science, concentrating on Control theory and frequently concerns with Probabilistic logic.

His most cited work include:

  • Decision-theoretic planning: structural assumptions and computational leverage (1017 citations)
  • A model for reasoning about persistence and causation (916 citations)
  • An analysis of time-dependent planning (776 citations)

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

Artificial intelligence, Mathematical optimization, Markov decision process, Robot and Machine learning are his primary areas of study. His Artificial intelligence research incorporates themes from Class, Computer vision and Natural language processing. His work on Approximation algorithm as part of general Mathematical optimization study is frequently linked to State, Special case and Time critical, therefore connecting diverse disciplines of science.

Many of his research projects under Markov decision process are closely connected to State space, Upper and lower bounds and Bounded function with State space, Upper and lower bounds and Bounded function, tying the diverse disciplines of science together. His Robotics and Mobile robot study in the realm of Robot interacts with subjects such as Control system. His study in Probabilistic logic is interdisciplinary in nature, drawing from both Management science and Causal reasoning.

He most often published in these fields:

  • Artificial intelligence (46.94%)
  • Mathematical optimization (20.41%)
  • Markov decision process (15.65%)

What were the highlights of his more recent work (between 2011-2019)?

  • Artificial intelligence (46.94%)
  • Dialog system (3.40%)
  • Communication (2.72%)

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

His primary areas of study are Artificial intelligence, Dialog system, Communication, Artificial neural network and Embodied agent. His biological study focuses on Models of neural computation. The study incorporates disciplines such as Human–computer interaction and User profile in addition to Dialog system.

His Artificial neural network research focuses on subjects like Neuroscience, which are linked to Scalability. Cognition is closely connected to Natural language understanding in his research, which is encompassed under the umbrella topic of Cognitive science. His biological study spans a wide range of topics, including Machine learning, Probabilistic logic, Projection and Causal reasoning.

Between 2011 and 2019, his most popular works were:

  • Fast, Accurate Detection of 100,000 Object Classes on a Single Machine (290 citations)
  • Contextual LSTM (CLSTM) models for Large scale NLP tasks (131 citations)
  • Neural networks and neuroscience-inspired computer vision. (99 citations)

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

  • Artificial intelligence
  • Machine learning
  • Programming language

Thomas Dean mainly investigates Artificial intelligence, Artificial neural network, Computer vision, Neuroscience and Systems neuroscience. His Artificial intelligence research includes elements of Decision problem and Markov chain. Thomas Dean undertakes interdisciplinary study in the fields of Artificial neural network and Sequence modeling through his research.

His Filter, Object detection, Contextual image classification and Object study, which is part of a larger body of work in Computer vision, is frequently linked to Object-class detection, bridging the gap between disciplines. His study deals with a combination of Neuroscience and Organism.

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

Decision-theoretic planning: structural assumptions and computational leverage

Craig Boutilier;Thomas Dean;Steve Hanks.
Journal of Artificial Intelligence Research (1999)

1481 Citations

An analysis of time-dependent planning

Thomas Dean;Mark Boddy.
national conference on artificial intelligence (1988)

1326 Citations

A model for reasoning about persistence and causation

Thomas Dean;Keiji Kanazawa.
computational intelligence (1989)

1261 Citations

Planning and Control

Thomas L. Dean;Michael P. Wellman.
(1991)

744 Citations

Temporal data base management

Thomas L. Dean;Drew McDermott.
Artificial Intelligence (1987)

549 Citations

On the complexity of solving Markov decision problems

Michael L. Littman;Thomas L. Dean;Leslie Pack Kaelbling.
uncertainty in artificial intelligence (1995)

525 Citations

Solving time-dependent planning problems

Mark Boddy;Thomas Dean.
international joint conference on artificial intelligence (1989)

512 Citations

Fast, Accurate Detection of 100,000 Object Classes on a Single Machine

Thomas Dean;Mark A. Ruzon;Mark Segal;Jonathon Shlens.
computer vision and pattern recognition (2013)

395 Citations

Bounded-parameter Markov decision process

Robert Givan;Sonia Leach;Thomas Dean.
Artificial Intelligence (2000)

366 Citations

Deliberation scheduling for problem solving in time-constrained environments

Mark Boddy;Thomas L. Dean.
Artificial Intelligence (1994)

353 Citations

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