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D-Index
38
Citations
58051
World Ranking
708
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38

Computer Science

D-Index
38
Citations
62545
World Ranking
9918
National Ranking
621

Caglar Gulcehre 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 Caglar Gulcehre 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 59 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.

Caglar Gulcehre 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 Caglar Gulcehre sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Caglar Gulcehre is a researcher affiliated with DeepMind in the United Kingdom. Their research contributions primarily lie within the field of Computer Science, with a focus on areas such as Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Computer Networks and Communications, and Control and Systems Engineering.

The scientist's work covers several main topics, including:

  • Reinforcement Learning in Robotics
  • Machine Learning and Algorithms
  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Bandit Algorithms Research
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications

Caglar Gulcehre has authored numerous publications, predominantly appearing in the venue arXiv (Cornell University), with a total of 39 publications in this outlet. Some recent papers 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)
  • Critic Regularized Regression, 2020, arXiv (Cornell University)
  • Acme: A Research Framework for Distributed Reinforcement Learning, 2020, arXiv (Cornell University)
  • Assessing Factoid Question-Answer Generation for Portuguese (Short Paper), 2020, arXiv (Cornell University)

The number of citations for some papers highlights their impact within the research community, such as 2380 citations for work on value selection heuristics and 1669 for static analysis in TensorFlow programs.

Frequent collaborators in their work include Razvan Pascanu, Nando de Freitas, Konrad Żołna, Tom Le Paine, and Ziyu Wang, with collaboration counts ranging from 5 to 10 joint publications.

Best Publications

  • Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation

    Kyunghyun Cho;Bart van Merrienboer;Caglar Gulcehre;Dzmitry Bahdanau

  • Empirical evaluation of gated recurrent neural networks on sequence modeling

    Junyoung Chung;Çaglar Gülçehre;KyungHyun Cho;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio

  • Grandmaster level in StarCraft II using multi-agent reinforcement learning.

    Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu

  • Relational inductive biases, deep learning, and graph networks

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

  • Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

    Ramesh Nallapati;Bowen Zhou;Cicero Nogueira dos santos;Caglar Gulcehre

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

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

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

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

  • How to Construct Deep Recurrent Neural Networks

    Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio

  • Gated Feedback Recurrent Neural Networks

    Junyoung Chung;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio;Yoshua Bengio

  • On using monolingual corpora in neural machine translation

    Çaglar Gülçehre;Orhan Firat;Kelvin Xu;Kyunghyun Cho

  • Pointing the unknown words

    Caglar Gulcehre;Sungjin Ahn;Ramesh Nallapati;Bowen Zhou

  • EmoNets: Multimodal deep learning approaches for emotion recognition in video

    Samira Ebrahimi Kahou;Xavier Bouthillier;Pascal Lamblin;Çaglar Gülçehre

  • Combining modality specific deep neural networks for emotion recognition in video

    Samira Ebrahimi Kahou;Christopher Pal;Xavier Bouthillier;Pierre Froumenty

  • Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

    Iulian Vlad Serban;Alberto García-Durán;Çaglar Gülçehre;Sungjin Ahn

  • Recurrent Batch Normalization

    Tim Cooijmans;Nicolas Ballas;César Laurent;Çaglar Gülçehre

  • Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

    Natasha Jaques;Angeliki Lazaridou;Edward Hughes;Çaglar Gülçehre

  • Noisy activation functions

    Caglar Gulcehre;Marcin Moczulski;Misha Denil;Yoshua Bengio

  • Learned-norm pooling for deep feedforward and recurrent neural networks

    Caglar Gulcehre;Kyunghyun Cho;Razvan Pascanu;Yoshua Bengio

  • Stabilizing Transformers for Reinforcement Learning

    Emilio Parisotto;Francis Song;Jack Rae;Razvan Pascanu

  • Machine Comprehension by Text-to-Text Neural Question Generation

    Xingdi Yuan;Tong Wang;Caglar Gulcehre;Alessandro Sordoni

  • Critic Regularized Regression

    Ziyu Wang;Alexander Novikov;Konrad Zolna;Jost Tobias Springenberg

  • Acme: A Research Framework for Distributed Reinforcement Learning

    Matt Hoffman;Bobak Shahriari;John Aslanides;Gabriel Barth-Maron

  • Machine Comprehension by Text-to-Text Neural Question Generation

    Xingdi Yuan;Tong Wang;Caglar Gulcehre;Alessandro Sordoni

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Kyunghyun Cho
Kyunghyun Cho New York University
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Aaron Courville
Aaron Courville University of Montreal
Adam Trischler
Adam Trischler Microsoft (United States)
Yann N. Dauphin
Yann N. Dauphin Google (United States)
Bowen Zhou
Bowen Zhou IBM (United States)
Raia Hadsell
Raia Hadsell DeepMind (United Kingdom)

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