Ying Jin

Artificial Intelligence · with Wang · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 319 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #31,494 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 71 citations into it; #19,345 of 21,575 by trust here, #15,529 by citations
• Representation & Self-Supervised Learning — 43 citations into it
• Meta & Continual Learning — 19 citations into it; #954 of 1,041 by trust here, #993 by citations
• Radiology, Nuclear Medicine and Imaging — 16 citations into it; #2,572 of 3,265 by trust here, #2,914 by citations
• Model Architectures — 8 citations into it
• Optimization & Training — 7 citations into it
• Efficiency & Compression — 5 citations into it

Whom Ying Jin trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Mingsheng Long
5. Yoshua Bengio
6. Yann LeCun
7. Diederik P. Kingma
8. Ilya Sutskever
9. Ross B. Girshick
10. Jia Deng

Who trusts Ying Jin overall

1. Lukas Hoyer
2. Dian Chen
3. Wen Zhang
4. Francesco Pelosin
5. Dengxin Dai
6. Dequan Wang
7. Lingfei Deng

Papers from Ying Jin's perspective

Ordered by how strongly the researchers Ying Jin 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. 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. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Minimum Class Confusion for Versatile Domain Adaptation (2019) — 478 citations
• Improving Vision Transformers for Incremental Learning (2021) — 18 citations