World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
39157
World Ranking
7338
National Ranking
3194

Edouard Grave 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 Edouard Grave 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: 74 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Edouard Grave 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 Edouard Grave 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Edouard Grave is affiliated with Facebook in the United States. Their research primarily falls within the field of Computer Science, with a significant focus on Artificial Intelligence and computer vision-related areas.

The subfields of study that characterize their work include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Communication
  • Information Systems
  • Software

The main topics addressed by Edouard Grave's research encompass:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Wikis in Education and Collaboration
  • Advanced Neural Network Applications
  • Speech Recognition and Synthesis

Edouard Grave has published extensively, with a predominant number of publications appearing in the following venues:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Nature Machine Intelligence
  • Research Square (Research Square)

Among their recent papers are:

  • LLaMA: Open and Efficient Foundation Language Models, 2023, arXiv (Cornell University)
  • ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Beyond English-Centric Multilingual Machine Translation, 2020, arXiv (Cornell University)
  • Atlas: Few-shot Learning with Retrieval Augmented Language Models, 2022, arXiv (Cornell University)
  • Unsupervised Dense Information Retrieval with Contrastive Learning, 2021, arXiv (Cornell University)

Frequent collaborators of Edouard Grave include:

  • Gautier Izacard
  • Armand Joulin
  • Fabio Petroni
  • Timo Schick
  • Patrick Lewis

Best Publications

  • Enriching Word Vectors with Subword Information

    Piotr Bojanowski;Edouard Grave;Armand Joulin;Tomas Mikolov

  • LLaMA: Open and Efficient Foundation Language Models

    Unknown

  • Bag of Tricks for Efficient Text Classification

    Armand Joulin;Edouard Grave;Piotr Bojanowski;Tomas Mikolov

  • Unsupervised Cross-lingual Representation Learning at Scale

    Alexis Conneau;Kartikay Khandelwal;Naman Goyal;Vishrav Chaudhary

  • Advances in Pre-Training Distributed Word Representations

    Tomas Mikolov;Edouard Grave;Piotr Bojanowski;Christian Puhrsch

  • Learning Word Vectors for 157 Languages

    Edouard Grave;Piotr Bojanowski;Prakhar Gupta;Armand Joulin

  • FastText.zip: Compressing text classification models

    Armand Joulin;Edouard Grave;Piotr Bojanowski;Matthijs Douze

  • ResMLP: Feedforward networks for image classification with data-efficient training

    Hugo Touvron;Piotr Bojanowski;Mathilde Caron;Matthieu Cord

  • Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

    Gautier Izacard;Edouard Grave

  • Parseval networks: improving robustness to adversarial examples

    Moustapha Cisse;Piotr Bojanowski;Edouard Grave;Yann Dauphin

  • Colorless green recurrent networks dream hierarchically

    Kristina Gulordava;Piotr Bojanowski;Edouard Grave;Tal Linzen

  • Beyond English-Centric Multilingual Machine Translation

    Angela Fan;Shruti Bhosale;Holger Schwenk;Zhiyi Ma

  • Few-shot Learning with Retrieval Augmented Language Models

    Unknown

  • Reducing Transformer Depth on Demand with Structured Dropout

    Angela Fan;Edouard Grave;Armand Joulin

  • Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

    Armand Joulin;Piotr Bojanowski;Tomas Mikolov;Hervé Jégou

  • CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

    Guillaume Wenzek;Marie-Anne Lachaux;Alexis Conneau;Vishrav Chaudhary

  • Augmented Language Models: a Survey

    Unknown

  • Adaptive Attention Span in Transformers

    Sainbayar Sukhbaatar;Edouard Grave;Piotr Bojanowski;Armand Joulin

  • Improving Neural Language Models with a Continuous Cache.

    Edouard Grave;Armand Joulin;Nicolas Usunier

  • Efficient softmax approximation for GPUs

    Édouard Grave;Armand Joulin;Moustapha Cissé

  • End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures

    Gabriel Synnaeve;Qiantong Xu;Jacob Kahn;Edouard Grave

  • CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB

    Holger Schwenk;Guillaume Wenzek;Sergey Edunov;Edouard Grave

  • Efficient softmax approximation for GPUs

    Edouard Grave;Armand Joulin;Moustapha Cissé;David Grangier

Frequent Co-Authors

Armand Joulin
Armand Joulin Google (United States)
Piotr Bojanowski
Piotr Bojanowski Facebook (United States)
Tomas Mikolov
Tomas Mikolov Czech Technical University in Prague
Francis Bach
Francis Bach École Normale Supérieure
Hervé Jégou
Hervé Jégou Facebook (United States)
Alexis Conneau
Alexis Conneau Facebook (United States)
Noémie Elhadad
Noémie Elhadad Columbia University
Michael Auli
Michael Auli Facebook (United States)
Nicolas Usunier
Nicolas Usunier Facebook (United States)

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