Shen Yan

Computer Vision and Pattern Recognition · with Zhang · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 407 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 136 citations into it
• Artificial Intelligence — 126 citations into it
• Model Architectures — 32 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Neural Architecture Search & AutoML — 26 citations into it
• Optimization & Training — 18 citations into it
• Graph Learning — 11 citations into it
• Generative Models — 6 citations into it

Whom Shen Yan trusts overall

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

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

Who trusts Shen Yan overall

1. Colin White
2. Divyat Mahajan
3. Lucas Mansilla
4. Daehee Kim
5. Zehua Sun
6. Mostafa Dehghani
7. Yongming Rao
8. Zhihan Gao
9. Limin Wang
10. Bolin Ni

Papers from Shen Yan's perspective

Ordered by how strongly the researchers Shen Yan 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Multiview Transformers for Video Recognition (2022) — 292 citations
• HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking (2019) — 22 citations
• Improve Unsupervised Domain Adaptation with Mixup Training (2020) — 275 citations
• Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? (2020) — 126 citations