Yee-Wah Tsang

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 21 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

Whom Yee-Wah Tsang trusts overall

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

  1. Radford M. Neal
  2. Alex Krizhevsky
  3. Geoffrey E. Hinton
  4. Kaiming He
  5. Ilya Sutskever
  6. Andrea Vedaldi
  7. Yoshua Bengio
  8. Yann LeCun
  9. Dan Claudiu Ciresan

Who trusts Yee-Wah Tsang overall

  1. Anant Madabhushi

Papers from Yee-Wah Tsang's perspective

Ordered by how strongly the researchers Yee-Wah Tsang trusts endorse each paper. Same corpus, a different order for every researcher.

  1. ImageNet classification with deep convolutional neural networks
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. ImageNet: A large-scale hierarchical image database
  5. ImageNet Large Scale Visual Recognition Challenge
  6. Going deeper with convolutions
  7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  9. Gradient-based learning applied to document recognition
  10. Dropout: a simple way to prevent neural networks from overfitting

Their papers