Michael G. Rabbat

Computer Vision and Pattern Recognition · with Assran · 2023

A researcher in the Science corpus: 1 paper; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Michael G. Rabbat trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Kaiming He (in Computer Vision and Pattern Recognition)
  2. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  3. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  4. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  5. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)
  6. Jitendra Malik (in Computer Vision and Pattern Recognition)
  7. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  8. Alexei A. Efros (in Computer Vision and Pattern Recognition)
  9. Cordelia Schmid (in Computer Vision and Pattern Recognition)
  10. David G. Lowe (in Computer Vision and Pattern Recognition)

Papers from Michael G. Rabbat's perspective

Ordered by how strongly the researchers Michael G. Rabbat trusts endorse each paper. Same corpus, a different order for every researcher.

  1. ImageNet Large Scale Visual Recognition Challenge
  2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  3. Unsupervised Visual Representation Learning by Context Prediction
  4. Language Models are Few-Shot Learners (Neural Information Processing Systems)
  5. A Simple Framework for Contrastive Learning of Visual Representations (International Conference on Machine Learning)
  6. Dimensionality Reduction by Learning an Invariant Mapping
  7. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
  8. Colorful Image Colorization
  9. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
  10. Momentum Contrast for Unsupervised Visual Representation Learning (Computer Vision and Pattern Recognition)

Their papers