World's Best Scientists 2026 revealed!

Nicolas Ballas 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 Nicolas Ballas 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+

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

Nicolas Ballas 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 Nicolas Ballas 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+

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

Overview

Nicolas Ballas is affiliated with Facebook in the United States. Their research primarily focuses on computer science, with a strong emphasis on computer vision and pattern recognition and artificial intelligence.

Their main fields of study include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Media Technology
  • Electrical and Electronic Engineering
  • Cancer Research

Research topics covered in their work encompass:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Generative Adversarial Networks and Image Synthesis
  • Video Analysis and Summarization
  • Advanced Vision and Imaging

Nicolas Ballas has published extensively, with a significant number of papers appearing in the venue arXiv (Cornell University), contributing 30 publications. Other notable venues include the 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and the 2022 26th International Conference on Pattern Recognition (ICPR).

Representative recent publications include:

  • Static Analysis of Shape in TensorFlow Programs, 2020, arXiv (Cornell University)
  • DINOv2: Learning Robust Visual Features without Supervision, 2023, arXiv (Cornell University)
  • Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture, 2023, arXiv (Cornell University)
  • The Hidden Uniform Cluster Prior in Self-Supervised Learning, 2022, arXiv (Cornell University)

Frequent co-authors collaborating with Nicolas Ballas include:

  • Mahmoud Assran
  • Michael Rabbat
  • Ishan Misra
  • Piotr Bojanowski
  • P. Vincent

Best Publications

  • FitNets: Hints for Thin Deep Nets

    Adriana Romero;Nicolas Ballas;Samira Ebrahimi Kahou;Antoine Chassang

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

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • Describing Videos by Exploiting Temporal Structure

    Li Yao;Atousa Torabi;Kyunghyun Cho;Nicolas Ballas

  • A closer look at memorization in deep networks

    Devansh Arpit;Stanisław Jastrzębski;Nicolas Ballas;David Krueger

  • Delving Deeper into Convolutional Networks for Learning Video Representations

    Nicolas Ballas;Li Yao;Chris Pal;Aaron Courville

  • Three Factors Influencing Minima in SGD

    Stanislaw Jastrzebski;Zachary Kenton;Devansh Arpit;Nicolas Ballas

  • Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.

    David Krueger;Tegan Maharaj;János Kramár;Mohammad Pezeshki

  • Recurrent Batch Normalization

    Tim Cooijmans;Nicolas Ballas;César Laurent;Çaglar Gülçehre

  • Stochastic Gradient Push for Distributed Deep Learning

    Mahmoud Assran;Nicolas Loizou;Nicolas Ballas;Michael G. Rabbat

  • A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning

    Amy Zhang;Nicolas Ballas;Joelle Pineau

  • Improved Conditional VRNNs for Video Prediction

    Lluis Castrejon;Nicolas Ballas;Aaron Courville

  • Dynamic capacity networks

    Amjad Almahairi;Nicolas Ballas;Tim Cooijmans;Yin Zheng

  • A Dataset and Exploration of Models for Understanding Video Data through Fill-in-the-Blank Question-Answering

    Tegan Maharaj;Nicolas Ballas;Anna Rohrbach;Aaron Courville

  • Deep Nets Don't Learn via Memorization

    David Krueger;Nicolas Ballas;Stanislaw Jastrzebski;Devansh Arpit

  • Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis

    Thomas George;César Laurent;Xavier Bouthillier;Nicolas Ballas

  • Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention Mechanism

    Li Yao;Atousa Torabi;Kyunghyun Cho;Nicolas Ballas

  • Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments With Support Samples

    Mahmoud Assran;Mathilde Caron;Ishan Misra;Piotr Bojanowski

  • Residual Connections Encourage Iterative Inference

    Stanislaw Jastrzebski;Devansh Arpit;Nicolas Ballas;Vikas Verma

  • SloMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

    Jianyu Wang;Vinayak Tantia;Nicolas Ballas;Michael Rabbat

  • SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

    Jianyu Wang;Vinayak Tantia;Nicolas Ballas;Michael Rabbat

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Aaron Courville
Aaron Courville University of Montreal
Michael Rabbat
Michael Rabbat Facebook (United States)
Chris Pal
Chris Pal Polytechnique Montréal
Kyunghyun Cho
Kyunghyun Cho New York University
Amos Storkey
Amos Storkey University of Edinburgh
Pascal Vincent
Pascal Vincent Facebook (United States)
Hugo Larochelle
Hugo Larochelle Google (United States)
Alexander G. Hauptmann
Alexander G. Hauptmann Carnegie Mellon University
John R. Smith
John R. Smith IBM (United States)

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