Joan Serrat

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 78 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Joan Serrat trusts overall

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

  1. Alex Krizhevsky
  2. Yann LeCun
  3. Geoffrey E. Hinton
  4. Yoshua Bengio
  5. Kaiming He
  6. Ross B. Girshick
  7. Christian Szegedy
  8. Jia Deng
  9. Diederik P. Kingma
  10. Ilya Sutskever

Papers from Joan Serrat's perspective

Ordered by how strongly the researchers Joan Serrat 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: A large-scale hierarchical image database
  5. Going deeper with convolutions
  6. Learning Multiple Layers of Features from Tiny Images
  7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  8. Attention is All you Need (Neural Information Processing Systems)
  9. Long Short-Term Memory
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers