World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
42336
World Ranking
2670
National Ranking
155

Marc'Aurelio Ranzato 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 Marc'Aurelio Ranzato 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: 111 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

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

Marc'Aurelio Ranzato 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 Marc'Aurelio Ranzato 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: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Overview

Marc'Aurelio Ranzato is affiliated with DeepMind in the United Kingdom. Their research primarily spans the field of Computer Science, focusing on several interconnected subfields.

Their main areas of study include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Developmental and Educational Psychology
  • Computational Theory and Mathematics

Marc'Aurelio Ranzato's work covers a broad spectrum of topics in machine learning and related disciplines:

  • Topic Modeling
  • Domain Adaptation and Few-Shot Learning
  • Natural Language Processing Techniques
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Speech Recognition and Synthesis
  • Machine Learning and ELM

Their publication record includes papers in multiple venues, notably:

  • arXiv (Cornell University)
  • Trends in Cognitive Sciences
  • Transactions of the Association for Computational Linguistics
  • Dagstuhl Research Online Publication Server

Examples of recent papers by Marc'Aurelio Ranzato include:

  • The <scp>Flores-101</scp> Evaluation Benchmark for Low-Resource and Multilingual Machine Translation, 2022, Transactions of the Association for Computational Linguistics
  • The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation, 2021, arXiv (Cornell University)
  • Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions, 2021, arXiv (Cornell University)
  • Efficient Continual Learning with Modular Networks and Task-Driven Priors, 2020, arXiv (Cornell University)
  • Helpless infants are learning a foundation model, 2024, Trends in Cognitive Sciences

They frequently collaborate with several co-authors, including:

  • Arthur Szlam
  • Arthur Douillard
  • Jiajun Shen
  • Myle Ott
  • Andrei A. Rusu

Best Publications

  • DeepFace: Closing the Gap to Human-Level Performance in Face Verification

    Yaniv Taigman;Ming Yang;Marc'Aurelio Ranzato;Lior Wolf

  • Large Scale Distributed Deep Networks

    Jeffrey Dean;Greg Corrado;Rajat Monga;Kai Chen

  • What is the best multi-stage architecture for object recognition?

    Kevin Jarrett;Koray Kavukcuoglu;Marc'Aurelio Ranzato;Yann LeCun

  • Building high-level features using large scale unsupervised learning

    Marc'aurelio Ranzato;Rajat Monga;Matthieu Devin;Kai Chen

  • DeViSE: A Deep Visual-Semantic Embedding Model

    Andrea Frome;Greg S Corrado;Jon Shlens;Samy Bengio

  • Efficient Learning of Sparse Representations with an Energy-Based Model

    Marc'aurelio Ranzato;Christopher Poultney;Sumit Chopra;Yann L. Cun

  • Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition

    M.A. Ranzato;Fu Jie Huang;Y.-L. Boureau;Yann LeCun

  • Sequence Level Training with Recurrent Neural Networks

    Marc'Aurelio Ranzato;Sumit Chopra;Michael Auli;Wojciech Zaremba

  • Word translation without parallel data

    Guillaume Lample;Alexis Conneau;Marc'Aurelio Ranzato;Ludovic Denoyer

  • Gradient Episodic Memory for Continual Learning

    David Lopez-Paz;Marc'Aurelio Ranzato

  • Sparse Feature Learning for Deep Belief Networks

    Marc'aurelio Ranzato;Y-lan Boureau;Yann L. Cun

  • Predicting Parameters in Deep Learning

    Misha Denil;Babak Shakibi;Laurent Dinh;Marc'Aurelio Ranzato

  • Efficient Lifelong Learning with A-GEM

    Arslan Chaudhry;Marc'Aurelio Ranzato;Marcus Rohrbach;Mohamed Elhoseiny

  • On rectified linear units for speech processing

    M. D. Zeiler;M. Ranzato;R. Monga;M. Mao

  • Phrase-Based & Neural Unsupervised Machine Translation

    Guillaume Lample;Myle Ott;Alexis Conneau;Ludovic Denoyer

  • Unsupervised Machine Translation Using Monolingual Corpora Only

    Guillaume Lample;Alexis Conneau;Ludovic Denoyer;Marc'Aurelio Ranzato

  • PANDA: Pose Aligned Networks for Deep Attribute Modeling

    Ning Zhang;Manohar Paluri;Marc'Aurelio Ranzato;Trevor Darrell

  • Video (language) modeling: a baseline for generative models of natural videos.

    Marc'Aurelio Ranzato;Arthur Szlam;Joan Bruna;Michaël Mathieu

  • Fader Networks:Manipulating Images by Sliding Attributes

    Guillaume Lample;Guillaume Lample;Neil Zeghidour;Nicolas Usunier;Antoine Bordes

  • Learning invariant features through topographic filter maps

    Koray Kavukcuoglu;Marc Aurelio Ranzato;Rob Fergus;Yann Le-Cun

  • On Tiny Episodic Memories in Continual Learning

    Arslan Chaudhry;Marcus Rohrbach;Mohamed Elhoseiny;Thalaiyasingam Ajanthan

Frequent Co-Authors

Myle Ott
Myle Ott Facebook (United States)
Ludovic Denoyer
Ludovic Denoyer Sorbonne University
Sumit Chopra
Sumit Chopra New York University
Arthur Szlam
Arthur Szlam DeepMind (United Kingdom)
Michael Auli
Michael Auli Facebook (United States)
Yann LeCun
Yann LeCun Facebook (United States)
Jeffrey Dean
Jeffrey Dean Google (United States)
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Andrew W. Senior
Andrew W. Senior Google (United States)

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