Kun Yao

Atomic and Molecular Physics, and Optics · with Parkhill · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Kun Yao trusts overall

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

  1. J. Behler
  2. Matthias Rupp 
  3. Sergei Manzhos
  4. Johannes Hachmann
  5. R. Ramakrishnan
  6. Geoffrey E. Hinton
  7. A. P. Bart'ok
  8. Alexandre Tkatchenko
  9. Katja Hansen
  10. Kristof T. Schütt

Who trusts Kun Yao overall

  1. Jared D. Willard
  2. Linfeng Zhang
  3. Xiaowei Jia
  4. Justin S. Smith
  5. J. Elm
  6. Pascal Friederich
  7. Shaoming Xu
  8. J. Behler
  9. Oliver T. Unke
  10. Nicholas Lubbers

Papers from Kun Yao's perspective

Ordered by how strongly the researchers Kun Yao 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. Gradient-based learning applied to document recognition
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  5. ImageNet: A large-scale hierarchical image database
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Long Short-Term Memory
  8. Learning Multiple Layers of Features from Tiny Images
  9. ImageNet Large Scale Visual Recognition Challenge
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers