Sylvestre-Alvise Rebuffi

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 780 researchers and is trusted by 760 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 233 citations into it; #671 of 35,345 by trust here, #791 by citations
• Computer Vision and Pattern Recognition — 189 citations into it; #2,020 of 21,575 by trust here, #2,814 by citations
• Safety, Robustness & Interpretability — 84 citations into it
• Representation & Self-Supervised Learning — 63 citations into it; #107 of 3,145 by trust here, #103 by citations
• Optimization & Training — 57 citations into it
• Model Architectures — 32 citations into it
• Generative Models — 17 citations into it
• Molecular Biology — 17 citations into it

Whom Sylvestre-Alvise Rebuffi trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Ian J. Goodfellow
9. Christian Szegedy
10. Ross B. Girshick

Who trusts Sylvestre-Alvise Rebuffi overall

1. Ali Ayub
2. Rahaf Aljundi
3. Da-Wei Zhou
4. Tyler L. Hayes
5. Xialei Liu
6. Vincenzo Lomonaco
7. Timothée Lesort
8. Gyuhak Kim
9. Marc Masana
10. Yaoyao Liu

Papers from Sylvestre-Alvise Rebuffi's perspective

Ordered by how strongly the researchers Sylvestre-Alvise Rebuffi 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• Learning multiple visual domains with residual adapters (2017) — 1,069 citations
• Efficient Parametrization of Multi-domain Deep Neural Networks (2018) — 387 citations
• iCaRL: Incremental Classifier and Representation Learning (2016) — 4,930 citations
• Automatically Discovering and Learning New Visual Categories with Ranking Statistics (2020) — 227 citations
• Improving Robustness using Generated Data (2021) — 379 citations
• Fixing Data Augmentation to Improve Adversarial Robustness (2021) — 319 citations
• Data Augmentation Can Improve Robustness (2021) — 411 citations