Vincent Perot

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 3 papers; trusts 54 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it; #33,023 of 35,345 by trust here, #6,266 by citations
• Computer Vision and Pattern Recognition — 78 citations into it
• Meta & Continual Learning — 44 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Safety, Robustness & Interpretability — 25 citations into it; #2,085 of 2,251 by trust here, #1,148 by citations
• Natural Language Processing — 17 citations into it; #5,315 of 5,688 by trust here, #2,808 by citations
• Reinforcement Learning — 7 citations into it

Whom Vincent Perot trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Marc'Aurelio Ranzato
9. Moritz Hardt
10. Ian J. Goodfellow

Who trusts Vincent Perot overall

1. Anthony Rios
2. Svetlana Kiritchenko
3. Ben Hutchinson
4. Bertie Vidgen
5. Frederick Liu
6. Vinodkumar Prabhakaran

Papers from Vincent Perot's perspective

Ordered by how strongly the researchers Vincent Perot 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning to Prompt for Continual Learning (2021) — 1,330 citations
• Counterfactual Fairness in Text Classification through Robustness (2018) — 294 citations
• DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning (2022) — 880 citations