Charles Ollion

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 367 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #7,955 of 35,345 by trust here, #6,720 by citations
• Computer Vision and Pattern Recognition — 106 citations into it; #5,856 of 21,575 by trust here, #4,955 by citations
• Representation & Self-Supervised Learning — 35 citations into it
• Natural Language Processing — 32 citations into it
• Reinforcement Learning — 31 citations into it
• Meta & Continual Learning — 27 citations into it; #219 of 1,041 by trust here, #153 by citations
• Radiology, Nuclear Medicine and Imaging — 17 citations into it; #350 of 3,265 by trust here, #486 by citations
• Optimization & Training — 15 citations into it

Whom Charles Ollion trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. David G. Lowe
9. Ross B. Girshick
10. Ronald J. Williams

Who trusts Charles Ollion overall

1. Arthur Douillard
2. Yaoyao Liu
3. Tyler L. Hayes
4. Da-Wei Zhou
5. Francesco Pelosin
6. Bernt Schiele
7. Shipeng Yan
8. Hanbin Zhao
9. Timothée Lesort
10. Liyuan Wang

Papers from Charles Ollion's perspective

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

Their papers

• PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (2020) — 909 citations
• Learning Natural Language Generation with Truncated Reinforcement Learning (2022) — 9 citations
• Insights from the Future for Continual Learning (2020) — 12 citations
• Small-Task Incremental Learning (2020) — 7 citations