James Tompkin

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 239 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom James Tompkin trusts overall

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

  1. Kaiming He
  2. Geoffrey E. Hinton
  3. Ming-Yu Liu
  4. Christian Szegedy
  5. Alex Krizhevsky
  6. Diederik P. Kingma
  7. Yoshua Bengio
  8. Yann LeCun
  9. Sergey Ioffe
  10. Ross B. Girshick

Papers from James Tompkin's perspective

Ordered by how strongly the researchers James Tompkin 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  7. Attention is All you Need (Neural Information Processing Systems)
  8. Long Short-Term Memory
  9. GENERATIVE ADVERSARIAL NETS
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers