Hongming Shan

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 3 papers; trusts 94 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it
• Artificial Intelligence — 49 citations into it
• Radiology, Nuclear Medicine and Imaging — 33 citations into it
• Biomedical Engineering — 24 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Optimization & Training — 11 citations into it

Whom Hongming Shan trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ge Wang
4. Alex Krizhevsky
5. Karen Simonyan
6. Jimmy Ba
7. Yoshua Bengio
8. Kaiming He
9. Yann LeCun
10. Andrew Zisserman

Who trusts Hongming Shan overall

1. Khan Muhammad

Papers from Hongming Shan's perspective

Ordered by how strongly the researchers Hongming Shan 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Online Prototype Learning for Online Continual Learning (2023) — 82 citations
• 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (2018) — 392 citations
• Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction (2018) — 379 citations