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 70 Citations 88,798 147 World Ranking 827 National Ranking 498

Research.com Recognitions

Awards & Achievements

2019 - ACM Prize in Computing For breakthrough advances in computer game-playing

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Reinforcement learning

His scientific interests lie mostly in Reinforcement learning, Artificial intelligence, Artificial neural network, Domain and Human–computer interaction. His work on Reinforcement learning algorithm as part of general Reinforcement learning research is frequently linked to Set, thereby connecting diverse disciplines of science. His Artificial intelligence research includes elements of Machine learning and Computer Go.

His work carried out in the field of Domain brings together such families of science as Network architecture, Q-learning, Control and Function approximation. His Human–computer interaction study incorporates themes from Learning methods and Relevance. His research in Learning environment tackles topics such as Bellman equation which are related to areas like Value.

His most cited work include:

  • Human-level control through deep reinforcement learning (11046 citations)
  • Mastering the game of Go with deep neural networks and tree search (7255 citations)
  • Playing Atari with Deep Reinforcement Learning (4302 citations)

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

David Silver focuses on Artificial intelligence, Reinforcement learning, Machine learning, Artificial neural network and Bellman equation. David Silver mostly deals with Mobile robot in his studies of Artificial intelligence. David Silver has researched Reinforcement learning in several fields, including Domain, Human–computer interaction, Learning environment, Monte Carlo tree search and Bayesian probability.

His studies in Machine learning integrate themes in fields like Control and Bayes' theorem. His work on Supervised learning as part of general Artificial neural network study is frequently linked to Process, bridging the gap between disciplines. His Bellman equation study combines topics from a wide range of disciplines, such as Range, Dynamic programming and Representation.

He most often published in these fields:

  • Artificial intelligence (66.15%)
  • Reinforcement learning (53.12%)
  • Machine learning (21.88%)

What were the highlights of his more recent work (between 2019-2021)?

  • Reinforcement learning (53.12%)
  • Artificial intelligence (66.15%)
  • Artificial neural network (17.19%)

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

Reinforcement learning, Artificial intelligence, Artificial neural network, Machine learning and Set are his primary areas of study. He incorporates Reinforcement learning and Scalability in his research. David Silver has researched Artificial intelligence in several fields, including Tree, Function and Bellman equation.

His Artificial neural network research is multidisciplinary, incorporating perspectives in Learning progress, Decomposition and Measure. He combines subjects such as Data point and Model learning with his study of Machine learning. His Algorithm study incorporates themes from Domain and Function.

Between 2019 and 2021, his most popular works were:

  • Improved protein structure prediction using potentials from deep learning (518 citations)
  • Mastering Atari, Go, chess and shogi by planning with a learned model (54 citations)
  • Behaviour Suite for Reinforcement Learning (15 citations)

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

  • Artificial intelligence
  • Machine learning
  • Algorithm

The scientist’s investigation covers issues in Reinforcement learning, Artificial intelligence, Algorithm, Function and Deep learning. His work carried out in the field of Reinforcement learning brings together such families of science as Software engineering and Leverage. David Silver studies Range which is a part of Artificial intelligence.

The concepts of his Algorithm study are interwoven with issues in Value, Bootstrapping and Domain. He has included themes like Learning environment, Gradient descent and Hyperparameter in his Function study. His Deep learning research includes themes of Exploit and Decision problem.

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

Human-level control through deep reinforcement learning

Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu.
Nature (2015)

14732 Citations

Mastering the game of Go with deep neural networks and tree search

David Silver;Aja Huang;Christopher J. Maddison;Arthur Guez.
Nature (2016)

10387 Citations

Playing Atari with Deep Reinforcement Learning

Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves.
arXiv: Learning (2013)

6158 Citations

Mastering the game of Go without human knowledge

David Silver;Julian Schrittwieser;Karen Simonyan;Ioannis Antonoglou.
Nature (2017)

5426 Citations

Asynchronous methods for deep reinforcement learning

Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves.
international conference on machine learning (2016)

4898 Citations

Continuous control with deep reinforcement learning

Timothy P. Lillicrap;Jonathan J. Hunt;Alexander Pritzel;Nicolas Heess.
arXiv: Learning (2015)

3930 Citations

Deep reinforcement learning with double Q-Learning

Hado van Hasselt;Arthur Guez;David Silver.
national conference on artificial intelligence (2016)

2920 Citations

Deterministic Policy Gradient Algorithms

David Silver;Guy Lever;Nicolas Heess;Thomas Degris.
international conference on machine learning (2014)

2056 Citations

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou.
Science (2018)

1369 Citations

Prioritized Experience Replay

Tom Schaul;John Quan;Ioannis Antonoglou;David Silver.
international conference on learning representations (2016)

1148 Citations

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