Songchen Han

Computer Vision and Pattern Recognition · 2021–2023

A researcher in the Science corpus: 2 papers; trusts 98 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Songchen Han trusts overall

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

  1. A. Bovik
  2. Zhou Wang
  3. Diederik P. Kingma
  4. Kaiming He
  5. Geoffrey E. Hinton
  6. Alex Krizhevsky
  7. Kin Gwn Lore
  8. Anish Mittal
  9. Olaf Ronneberger
  10. Wenhan Yang

Papers from Songchen Han's perspective

Ordered by how strongly the researchers Songchen Han 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. ImageNet: A large-scale hierarchical image database
  5. Attention is All you Need (Neural Information Processing Systems)
  6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  7. Gradient-based learning applied to document recognition
  8. ImageNet Large Scale Visual Recognition Challenge
  9. Long Short-Term Memory
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers