Tao Gong

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 296 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Tao Gong trusts overall

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

  1. Kaiming He
  2. Ross B. Girshick
  3. Geoffrey E. Hinton
  4. Alex Krizhevsky
  5. Shaoqing Ren
  6. Diederik P. Kingma
  7. Tsung-Yi Lin
  8. Yoshua Bengio
  9. Piotr Doll'ar
  10. Yann LeCun

Papers from Tao Gong's perspective

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

Their papers