Ying-Cong Chen

Computer Vision and Pattern Recognition · with Cheng · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 445 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 111 citations into it
• Artificial Intelligence — 101 citations into it
• Meta & Continual Learning — 46 citations into it
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Optimization & Training — 14 citations into it
• Natural Language Processing — 7 citations into it

Whom Ying-Cong Chen trusts overall

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

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

Papers from Ying-Cong Chen's perspective

Ordered by how strongly the researchers Ying-Cong Chen 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Representation Compensation Networks for Continual Semantic Segmentation (2022) — 143 citations
• Delving into Deep Imbalanced Regression (2021) — 409 citations