World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
53953
World Ranking
12329
National Ranking
781

Volodymyr Mnih publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Volodymyr Mnih sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 50 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Volodymyr Mnih D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Volodymyr Mnih sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Volodymyr Mnih is affiliated with DeepMind in the United Kingdom. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence and related subfields such as Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Computational Theory and Mathematics, and Management Science and Operations Research.

The scientist's main topics of work include Reinforcement Learning in Robotics, Domain Adaptation and Few-Shot Learning, Human Pose and Action Recognition, Adversarial Robustness in Machine Learning, Explainable Artificial Intelligence (XAI), Smart Grid Energy Management, and Adaptive Dynamic Programming Control.

Volodymyr Mnih has published in several venues, with the majority of papers appearing in arXiv (Cornell University). Additionally, there are contributions in the Proceedings of the AAAI Conference on Artificial Intelligence.

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence

Recent papers authored by or involving Volodymyr Mnih include:

  • "Q-Learning in enormous action spaces via amortized approximate maximization," 2020, arXiv (Cornell University)
  • "Q-Learning in enormous action spaces via amortized approximate maximization," 2020, arXiv (Cornell University)
  • "In-context Reinforcement Learning with Algorithm Distillation," 2022, arXiv (Cornell University)
  • "Relative Variational Intrinsic Control," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Relative Variational Intrinsic Control," 2020, arXiv (Cornell University)

Frequent co-authors collaborating with Volodymyr Mnih include:

  • David Warde-Farley
  • Kate Baumli
  • Steven Hansen
  • Stephen Spencer
  • Maxime Gazeau

Best Publications

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • Playing Atari with Deep Reinforcement Learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves

  • Asynchronous methods for deep reinforcement learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Recurrent Models of Visual Attention

    Volodymyr Mnih;Nicolas Heess;Alex Graves;koray kavukcuoglu

  • Recurrent Models of Visual Attention

    Volodymyr Mnih;Nicolas Heess;Alex Graves;Koray Kavukcuoglu

  • Asynchronous Methods for Deep Reinforcement Learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Reinforcement Learning with Unsupervised Auxiliary Tasks

    Max Jaderberg;Volodymyr Mnih;Wojciech Marian Czarnecki;Tom Schaul

  • Multiple Object Recognition with Visual Attention

    Jimmy Lei Ba;Volodymyr Mnih;Koray Kavukcuoglu

  • IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

    Lasse Espeholt;Hubert Soyer;Remi Munos;Karen Simonyan

  • Machine learning for aerial image labeling

    Geoffrey Hinton;Volodymyr Mnih

  • Learning to detect roads in high-resolution aerial images

    Volodymyr Mnih;Geoffrey E. Hinton

  • Multiple Object Recognition with Visual Attention

    Jimmy Ba;Volodymyr Mnih;Koray Kavukcuoglu

  • Massively Parallel Methods for Deep Reinforcement Learning

    Arun Nair;Praveen Srinivasan;Sam Blackwell;Cagdas Alcicek

  • Sample Efficient Actor-Critic with Experience Replay.

    Ziyu Wang;Victor Bapst;Nicolas Heess;Volodymyr Mnih

  • Learning to Label Aerial Images from Noisy Data

    Volodymyr Mnih;Geoffrey E. Hinton

  • Learning by Playing - Solving Sparse Reward Tasks from Scratch

    Martin A. Riedmiller;Roland Hafner;Thomas Lampe;Michael Neunert

  • On deep generative models with applications to recognition

    Marc'Aurelio Ranzato;Joshua Susskind;Volodymyr Mnih;Geoffrey Hinton

  • Empirical Bernstein stopping

    Volodymyr Mnih;Csaba Szepesvári;Jean-Yves Audibert

  • Sample Efficient Actor-Critic with Experience Replay

    Ziyu Wang;Victor Bapst;Nicolas Heess;Volodymyr Mnih

  • Combining policy gradient and Q-learning

    Brendan O'Donoghue;Remi Munos;Koray Kavukcuoglu;Volodymyr Mnih

  • Using Fast Weights to Attend to the Recent Past

    Jimmy Ba;Geoffrey E. Hinton;Volodymyr Mnih;Joel Z. Leibo

  • Policy Distillation

    Andrei A. Rusu;Sergio Gomez Colmenarejo;Caglar Gulcehre;Guillaume Desjardins

  • Conditional restricted Boltzmann machines for structured output prediction

    Volodymyr Mnih;Hugo Larochelle;Geoffrey E. Hinton

  • The Uncertainty Bellman Equation and Exploration.

    Brendan O'Donoghue;Ian Osband;Rémi Munos;Volodymyr Mnih

  • Unsupervised Learning of Object Keypoints for Perception and Control

    Tejas D. Kulkarni;Ankush Gupta;Catalin Ionescu;Sebastian Borgeaud

  • Learning by Playing - Solving Sparse Reward Tasks from Scratch

    Martin Riedmiller;Roland Hafner;Thomas Lampe;Michael Neunert

Frequent Co-Authors

Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
David Silver
David Silver DeepMind (United Kingdom)
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Alex Graves
Alex Graves Google (United States)
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Martin Riedmiller
Martin Riedmiller DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)

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