Yunsheng Li

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 4 papers; trusts 293 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #10,601 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 75 citations into it; #7,202 of 21,575 by trust here, #10,010 by citations
• Evaluation & Benchmarks — 32 citations into it
• Model Architectures — 17 citations into it
• Natural Language Processing — 12 citations into it
• Representation & Self-Supervised Learning — 7 citations into it; #1,252 of 3,145 by trust here, #1,843 by citations
• Optimization & Training — 6 citations into it
• Generative Models — 5 citations into it

Whom Yunsheng Li trusts overall

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

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

Who trusts Yunsheng Li overall

1. Yanchao Yang
2. Christos Sakaridis
3. Dengxin Dai
4. Lukas Hoyer
5. Arthur Douillard
6. Jiahua Dong
7. Qiming Zhang
8. Minjie Cai
9. Wilhelm Tranheden
10. Luc Van Gool

Papers from Yunsheng Li's perspective

Ordered by how strongly the researchers Yunsheng Li 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Bidirectional Learning for Domain Adaptation of Semantic Segmentation (2019) — 688 citations
• Improving Code Localization with Repository Memory (2025)
• Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs (2025) — 509 citations
• Revisiting Dynamic Convolution via Matrix Decomposition (2021) — 86 citations