Hanine Tout

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Hanine Tout trusts overall

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

  1. Alex Krizhevsky
  2. H. Brendan McMahan
  3. Yann LeCun
  4. Jakub Konecný
  5. Geoffrey E. Hinton
  6. Yoshua Bengio
  7. L. Eon Bottou
  8. Keith Bonawitz
  9. Robin C. Geyer
  10. Boyi Liu

Papers from Hanine Tout's perspective

Ordered by how strongly the researchers Hanine Tout trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Gradient-based learning applied to document recognition
  2. Learning Multiple Layers of Features from Tiny Images
  3. ImageNet classification with deep convolutional neural networks
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  6. Long Short-Term Memory
  7. ImageNet: A large-scale hierarchical image database
  8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  9. Attention is All you Need (Neural Information Processing Systems)
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers