World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
71
Citations
44558
World Ranking
1723
National Ranking
95

Razvan Pascanu 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 Razvan Pascanu 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: 163 publications — 32nd percentile

32% of scientists in this discipline score the same or lower.

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

Razvan Pascanu 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 Razvan Pascanu 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: 71 D-Index — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Overview

Razvan Pascanu is a researcher affiliated with DeepMind in the United Kingdom. Their work spans a considerable body of research primarily focused in the field of computer science, with a specialization in artificial intelligence and related subfields. The scientist's research contributions are extensively documented in various academic publications, demonstrating engagement with several interconnected topics within machine learning and neural networks.

The primary area of study for Razvan Pascanu is computer science, with notable subfields including artificial intelligence, computer vision and pattern recognition, materials chemistry, computational theory and mathematics, as well as computer networks and communications. Their research interests also encompass specific topics such as domain adaptation and few-shot learning, multimodal machine learning applications, reinforcement learning in robotics, advanced neural network applications, topic modeling, neural networks and applications, and adversarial robustness in machine learning.

Significant recent publications by Razvan Pascanu include:

  • "Understanding the Impact of Value Selection Heuristics in Scheduling Problems," 2025, arXiv (Cornell University)
  • "Static Analysis of Shape in TensorFlow Programs," 2020, arXiv (Cornell University)
  • "Embracing Change: Continual Learning in Deep Neural Networks," 2020, Trends in Cognitive Sciences
  • "Distral: Robust Multitask Reinforcement Learning," 2025, Oxford University Research Archive (ORA) (University of Oxford)
  • "Understanding the Role of Training Regimes in Continual Learning," 2020, arXiv (Cornell University)

Razvan Pascanu frequently collaborates with several co-authors who have contributed notably to machine learning. Frequent co-authors include Yee Whye Teh, Çaǧlar Gülçehre, Petar Veličković, Soham De, and Jörg Bornschein.

The researcher regularly publishes in prominent venues, predominantly in arXiv (Cornell University), with 80 publications there. Other publication venues include Trends in Cognitive Sciences, IEEE Transactions on Pattern Analysis and Machine Intelligence, Nature, and Oxford University Research Archive (ORA) (University of Oxford).

Best Publications

  • Overcoming catastrophic forgetting in neural networks

    James Kirkpatrick;Razvan Pascanu;Neil C. Rabinowitz;Joel Veness

  • On the difficulty of training recurrent neural networks

    Razvan Pascanu;Tomas Mikolov;Yoshua Bengio

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • Theano: A CPU and GPU Math Compiler in Python

    James Bergstra;Olivier Breuleux;Frédéric Bastien;Pascal Lamblin

  • Theano: new features and speed improvements

    Frédéric Bastien;Pascal Lamblin;Razvan Pascanu;James Bergstra

  • A simple neural network module for relational reasoning

    Adam Santoro;David Raposo;David G. T. Barrett;Mateusz Malinowski

  • Progressive neural networks

    Neil Charles Rabinowitz;Guillaume Desjardins;Andrei-Alexandru Rusu;Koray Kavukcuoglu

  • Identifying and attacking the saddle point problem in high-dimensional non-convex optimization

    Yann N Dauphin;Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho

  • On the Number of Linear Regions of Deep Neural Networks

    Guido F Montufar;Razvan Pascanu;Kyunghyun Cho;Yoshua Bengio

  • Interaction networks for learning about objects, relations and physics

    Peter Battaglia;Razvan Pascanu;Matthew Lai;Danilo Jimenez Rezende

  • How to Construct Deep Recurrent Neural Networks

    Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio

  • Meta-Learning with Latent Embedding Optimization

    Andrei A. Rusu;Dushyant Rao;Jakub Sygnowski;Oriol Vinyals

  • Vector-based navigation using grid-like representations in artificial agents

    Andrea Banino;Caswell Barry;Benigno Uria;Charles Blundell

  • Advances in optimizing recurrent networks

    Yoshua Bengio;Nicolas Boulanger-Lewandowski;Razvan Pascanu

  • Learning to Navigate in Complex Environments

    Piotr Mirowski;Razvan Pascanu;Fabio Viola;Hubert Soyer

  • Understanding the exploding gradient problem

    Razvan Pascanu;Tomas Mikolov;Yoshua Bengio

  • Learning Deep Generative Models of Graphs

    Yujia Li;Oriol Vinyals;Chris Dyer;Razvan Pascanu

  • Model compression via distillation and quantization

    Antonio Polino;Razvan Pascanu;Dan Alistarh

  • Malware classification with recurrent networks

    Razvan Pascanu;Jack W. Stokes;Hermineh Sanossian;Mady Marinescu

  • Progress & Compress: A scalable framework for continual learning

    Jonathan Schwarz;Jelena Luketina;Wojciech M. Czarnecki;Agnieszka Grabska-Barwinska

  • Imagination-Augmented Agents for Deep Reinforcement Learning

    Sébastien Racanière;Theophane Weber;David P. Reichert;Lars Buesing

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Raia Hadsell
Raia Hadsell DeepMind (United Kingdom)
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)
Yee Whye Teh
Yee Whye Teh University of Oxford
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Kyunghyun Cho
Kyunghyun Cho New York University

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