Li Shen

Artificial Intelligence · with Wang · 2022–2024

A researcher in the Science corpus: 5 papers; trusts 212 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it
• Computer Vision and Pattern Recognition — 133 citations into it
• Meta & Continual Learning — 77 citations into it
• Representation & Self-Supervised Learning — 62 citations into it
• Optimization & Training — 26 citations into it
• Statistics and Probability — 14 citations into it
• Reinforcement Learning — 13 citations into it

Whom Li Shen 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. Kaiming He
6. Marc'Aurelio Ranzato
7. Arslan Chaudhry
8. Yann LeCun
9. Oriol Vinyals
10. Ilya Sutskever

Who trusts Li Shen overall

1. Zhenyi Wang
2. Liyuan Wang
3. Liangzu Peng

Papers from Li Shen's perspective

Ordered by how strongly the researchers Li Shen 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

• A Unified and General Framework for Continual Learning (2024) — 48 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
• Meta-learning without data via Wasserstein distributionally-robust model fusion (2022) — 24 citations