Stéphane Pateux

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 277 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Optimization & Training — 10 citations into it
• Neural Architecture Search & AutoML — 9 citations into it

Whom Stéphane Pateux trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Barret Zoph
7. Yann LeCun
8. Ilya Sutskever
9. Hugo Larochelle
10. Sepp Hochreiter

Papers from Stéphane Pateux's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Efficient Progressive Neural Architecture Search (2018) — 44 citations
• Leveraging the Feature Distribution in Transfer-based Few-Shot Learning (2020) — 189 citations