Mehdi Abbana Bennani

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 199 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #9,238 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #6,507 of 21,575 by trust here, #10,010 by citations
• Optimization & Training — 25 citations into it
• Meta & Continual Learning — 19 citations into it; #258 of 1,041 by trust here, #529 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Model Architectures — 9 citations into it
• Reinforcement Learning — 7 citations into it
• Computational Mechanics — 6 citations into it

Whom Mehdi Abbana Bennani trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Marc'Aurelio Ranzato
6. Arslan Chaudhry
7. Gido M. van de Ven
8. Ilya Sutskever
9. Sergey Ioffe
10. Diederik P. Kingma

Who trusts Mehdi Abbana Bennani overall

1. Ryo Karakida
2. Liangzu Peng
3. Xinyuan Cao
4. Gyuhak Kim
5. Thang Van Doan
6. Shotaro Akaho
7. Itay Evron
8. Krishnan Raghavan
9. Paris V. Giampouras
10. Weiyang Liu

Papers from Mehdi Abbana Bennani's perspective

Ordered by how strongly the researchers Mehdi Abbana Bennani trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix (2020) — 120 citations
• Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent (2020) — 85 citations