Jean-Baptiste Cordonnier

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 591 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 144 citations into it; #4,664 of 35,345 by trust here, #5,317 by citations
• Computer Vision and Pattern Recognition — 118 citations into it; #8,682 of 21,575 by trust here, #6,954 by citations
• Model Architectures — 79 citations into it; #855 of 2,278 by trust here, #810 by citations
• Optimization & Training — 44 citations into it; #630 of 2,601 by trust here, #999 by citations
• Natural Language Processing — 30 citations into it
• Efficiency & Compression — 23 citations into it; #210 of 1,264 by trust here, #561 by citations
• Computational Mechanics — 20 citations into it; #1,219 of 10,001 by trust here, #1,745 by citations
• Large Language Models — 15 citations into it

Whom Jean-Baptiste Cordonnier trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Yann LeCun
9. Ashish Vaswani
10. Sepp Hochreiter

Who trusts Jean-Baptiste Cordonnier overall

1. Sebastian U. Stich
2. Anastasia Koloskova
3. Yihe Dong
4. Martin Jaggi
5. Gedas Bertasius
6. Tianyang Lin
7. Hanlin Tang
8. Yujin Tang
9. Sai Praneeth Karimireddy
10. Kaleel Mahmood

Papers from Jean-Baptiste Cordonnier's perspective

Ordered by how strongly the researchers Jean-Baptiste Cordonnier 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• On the Relationship between Self-Attention and Convolutional Layers (2019) — 645 citations
• Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth (2021) — 569 citations
• Sparsified SGD with Memory (2018) — 894 citations
• Differentiable Patch Selection for Image Recognition (2021) — 123 citations
• Multi-Head Attention: Collaborate Instead of Concatenate (2020) — 180 citations
• Group Equivariant Stand-Alone Self-Attention For Vision (2020) — 77 citations