Kyle Heath

Computer Vision and Pattern Recognition · 2010

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Kyle Heath trusts overall

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

  1. David G. Lowe
  2. Ondřej Chum
  3. Herv'e J'egou
  4. Cordelia Schmid
  5. Sameer Agarwal
  6. Matthijs Douze
  7. Andrew Zisserman
  8. Yantao Zheng
  9. James Philbin
  10. Josef Sivic

Who trusts Kyle Heath overall

  1. Yannis Avrithis
  2. Yin Lou
  3. Yannis Kalantidis
  4. Noah Snavely

Papers from Kyle Heath's perspective

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

  1. Distinctive Image Features from Scale-Invariant Keypoints
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. ImageNet classification with deep convolutional neural networks
  4. ImageNet: A large-scale hierarchical image database
  5. Gradient-based learning applied to document recognition
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. ImageNet Large Scale Visual Recognition Challenge
  8. Long Short-Term Memory
  9. Going deeper with convolutions
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers