Le Fang

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 4 papers; trusts 184 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 164 citations into it; #21,496 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 138 citations into it; #13,381 of 21,575 by trust here, #15,955 by citations
• Meta & Continual Learning — 76 citations into it
• Representation & Self-Supervised Learning — 62 citations into it
• Optimization & Training — 26 citations into it
• Reinforcement Learning — 13 citations into it
• Statistics and Probability — 11 citations into it

Whom Le Fang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Marc'Aurelio Ranzato
6. Yann LeCun
7. Kaiming He
8. Oriol Vinyals
9. Ilya Sutskever
10. Rahaf Aljundi

Who trusts Le Fang overall

1. Zhenyi Wang
2. Li Shen

Papers from Le Fang's perspective

Ordered by how strongly the researchers Le Fang 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. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness (2021) — 21 citations
• Learning to Learn and Remember Super Long Multi-Domain Task Sequence (2022) — 27 citations
• Improving Task-free Continual Learning by Distributionally Robust Memory Evolution (2022) — 54 citations
• Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions (2022) — 22 citations