Paul Michel

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 292 researchers and is trusted by 72 researchers here, as inferred from the citation graph.

Subfields

Whom Paul Michel trusts overall

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

  1. Geoffrey E. Hinton
  2. Diederik P. Kingma
  3. Alex Krizhevsky
  4. Yoshua Bengio
  5. Ilya Sutskever
  6. Yann LeCun
  7. Rico Sennrich
  8. Philipp Koehn
  9. Christian Szegedy
  10. Kaiming He

Who trusts Paul Michel overall

  1. Congzheng Song
  2. Canwen Xu
  3. J. Scott McCarley
  4. Sehoon Kim
  5. Vitaly Shmatikov
  6. Wangchunshu Zhou
  7. Victor Sanh
  8. Anna Rogers
  9. Zhengyan Zhang
  10. Olga Kovaleva

Papers from Paul Michel's perspective

Ordered by how strongly the researchers Paul Michel 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. Attention is All you Need (Neural Information Processing Systems)
  5. Long Short-Term Memory
  6. Gradient-based learning applied to document recognition
  7. ImageNet: A large-scale hierarchical image database
  8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
  10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers