James Traer

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom James Traer trusts overall

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

  1. Geoffrey E. Hinton
  2. Yoshua Bengio
  3. Radford M. Neal
  4. Kaiming He
  5. David G. Lowe
  6. Stéphane Mallat
  7. Alex Krizhevsky
  8. Yann LeCun
  9. Diederik P. Kingma
  10. Ilya Sutskever

Papers from James Traer's perspective

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

Their papers