Jun Yao

Computer Vision and Pattern Recognition · with Li · 2018

A researcher in the Science corpus: 1 paper; trusts 91 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jun Yao trusts overall

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

  1. Ross B. Girshick
  2. Kaiming He
  3. Alex Krizhevsky
  4. Shaoqing Ren
  5. Yann LeCun
  6. Geoffrey E. Hinton
  7. Karen Simonyan

Papers from Jun Yao's perspective

Ordered by how strongly the researchers Jun Yao 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. Gradient-based learning applied to document recognition
  4. ImageNet Large Scale Visual Recognition Challenge
  5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  7. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
  8. ImageNet: A large-scale hierarchical image database
  9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  10. Long Short-Term Memory

Their papers