Baoyu Jing

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 378 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 45 citations into it; #7,064 of 21,575 by trust here, #10,677 by citations
• Artificial Intelligence — 37 citations into it; #10,293 of 35,345 by trust here, #15,866 by citations
• Natural Language Processing — 19 citations into it; #3,336 of 5,688 by trust here, #3,529 by citations
• Multimodal Learning — 17 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Reinforcement Learning — 10 citations into it
• Model Architectures — 8 citations into it
• Radiology, Nuclear Medicine and Imaging — 7 citations into it

Whom Baoyu Jing trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Oriol Vinyals
6. Ronald J. Williams
7. Chin-Yew Lin
8. Jimmy Ba
9. Alex Krizhevsky
10. Karen Simonyan

Who trusts Baoyu Jing overall

1. Zhihong Chen
2. Yuan Li
3. Jun Chen
4. Yasuhide Miura
5. Fenglin Liu
6. Xiaodan Liang
7. Yan Song
8. Aurelia Bustos
9. Yuhao Zhang
10. Xiaosong Wang

Papers from Baoyu Jing's perspective

Ordered by how strongly the researchers Baoyu Jing 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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

• On the Automatic Generation of Medical Imaging Reports (2017) — 673 citations
• Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports (2019) — 182 citations