Thomas Robert

Artificial Intelligence · 2020

A researcher in the Science corpus: 3 papers; trusts 245 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #7,956 of 35,345 by trust here, #6,848 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #5,855 of 21,575 by trust here, #4,955 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Meta & Continual Learning — 27 citations into it; #218 of 1,041 by trust here, #153 by citations
• Radiology, Nuclear Medicine and Imaging — 17 citations into it; #349 of 3,265 by trust here, #486 by citations
• Optimization & Training — 13 citations into it
• Reinforcement Learning — 8 citations into it
• Model Architectures — 6 citations into it

Whom Thomas Robert trusts overall

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

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

Who trusts Thomas Robert 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 Thomas Robert's perspective

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

Their papers

• PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (2020) — 909 citations
• Insights from the Future for Continual Learning (2020) — 12 citations
• Small-Task Incremental Learning (2020) — 7 citations