Shan You

Computer Vision and Pattern Recognition · 2017–2024

A researcher in the Science corpus: 10 papers; trusts 756 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 290 citations into it; #5,703 of 21,575 by trust here, #5,711 by citations
• Artificial Intelligence — 217 citations into it; #7,767 of 35,345 by trust here, #9,170 by citations
• Efficiency & Compression — 86 citations into it; #367 of 1,264 by trust here, #673 by citations
• Model Architectures — 60 citations into it
• Optimization & Training — 28 citations into it; #1,027 of 2,601 by trust here, #1,134 by citations
• Evaluation & Benchmarks — 27 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Signal Processing — 19 citations into it

Whom Shan You trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. Karen Simonyan
6. Yann LeCun
7. Ross B. Girshick
8. Diederik P. Kingma
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Shan You overall

1. Yunhe Wang
2. Xiaojie Li
3. Kai Han
4. Jianyuan Guo
5. Yuang Liu
6. Lin Wang
7. Manish Gupta
8. Shangchen Du
9. Chang Xu
10. Jianlong Wu

Papers from Shan You's perspective

Ordered by how strongly the researchers Shan You 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. 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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning from Multiple Teacher Networks (2017) — 464 citations
• The Seventh Visual Object Tracking VOT2019 Challenge Results (2019) — 486 citations
• LocalMamba: Visual State Space Model with Windowed Selective Scan (2024) — 430 citations
• Knowledge Distillation from A Stronger Teacher (2022) — 435 citations
• Reborn Filters: Pruning Convolutional Neural Networks with Limited Data (2020) — 33 citations
• Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space (2020) — 140 citations
• GreedyNAS: Towards Fast One-Shot NAS With Greedy Supernet (2020) — 153 citations
• Learning Student Networks with Few Data (2020) — 12 citations
• Bringing Giant Neural Networks Down to Earth with Unlabeled Data (2019) — 11 citations
• Learning With Single-Teacher Multi-Student (2018) — 29 citations