Tim Wang

Computer Vision and Pattern Recognition · with Michaelos · 2020

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Tim Wang trusts overall

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

  1. Kaiming He
  2. Simon Hessner - Supervised
  3. Olaf Ronneberger
  4. Leon A. Gatys
  5. Zhe Cao
  6. X. Zhang

Who trusts Tim Wang overall

  1. C. Stringer
  2. Marius Pachitariu

Papers from Tim Wang's perspective

Ordered by how strongly the researchers Tim Wang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  5. Gradient-based learning applied to document recognition
  6. ImageNet Large Scale Visual Recognition Challenge
  7. ImageNet: A large-scale hierarchical image database
  8. Going deeper with convolutions
  9. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers