Rebecca M. Willett

Computer Vision and Pattern Recognition · 2007–2020

A researcher in the Science corpus: 9 papers; trusts 136 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

Whom Rebecca M. Willett trusts overall

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

  1. Nicolò Cesa-Bianchi
  2. Lin Xiao
  3. Martin A. Zinkevich
  4. Mark Herbster
  5. Manfred K. Warmuth
  6. Gábor Lugosi
  7. E.J. Candes
  8. Nick Littlestone
  9. András György
  10. John C. Duchi

Who trusts Rebecca M. Willett overall

  1. Lijun Zhang
  2. David J. Brady
  3. Yawei Zhao
  4. Eric C. Hall
  5. Shahin Shahrampour
  6. Alexander Rainer Tassilo Gepperth
  7. András György
  8. Yawei Zhao
  9. Amrit Singh Bedi
  10. Xin Yuan

Papers from Rebecca M. Willett's perspective

Ordered by how strongly the researchers Rebecca M. Willett trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  2. ImageNet classification with deep convolutional neural networks
  3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. Gradient-based learning applied to document recognition
  5. Learning Multiple Layers of Features from Tiny Images
  6. ImageNet: A large-scale hierarchical image database
  7. Long Short-Term Memory
  8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  9. Attention is All you Need (Neural Information Processing Systems)
  10. GENERATIVE ADVERSARIAL NETS

Their papers