Guillaume Leclerc

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 126 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Optimization & Training — 31 citations into it
• Safety, Robustness & Interpretability — 27 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Natural Language Processing — 14 citations into it
• Management Science and Operations Research — 12 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Guillaume Leclerc trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. James Martens
4. Geoffrey E. Hinton
5. Kaiming He
6. Diederik P. Kingma
7. Yann LeCun
8. Radford M. Neal
9. Dong C. Liu
10. Ilya Sutskever

Papers from Guillaume Leclerc's perspective

Ordered by how strongly the researchers Guillaume Leclerc 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Datamodels: Predicting Predictions from Training Data (2022) — 215 citations
• TRAK: Attributing Model Behavior at Scale (2023) — 305 citations
• The Two Regimes of Deep Network Training (2020) — 52 citations