Ruiming Cao

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 664 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 120 citations into it; #5,777 of 21,575 by trust here, #8,205 by citations
• Artificial Intelligence — 35 citations into it; #7,971 of 35,345 by trust here, #12,244 by citations
• Safety, Robustness & Interpretability — 15 citations into it; #1,099 of 2,251 by trust here, #1,616 by citations
• Aerospace Engineering — 13 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 11 citations into it
• Biophysics — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it; #1,510 of 3,145 by trust here, #2,404 by citations

Whom Ruiming Cao trusts overall

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

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

Who trusts Ruiming Cao overall

1. Quanshi Zhang
2. Song-Chun Zhu
3. Ying Nian Wu
4. Giulia Vilone
5. Yu Yang
6. Wenguan Wang
7. Alexandre Heuillet
8. Chandan Singh
9. Stephan Alaniz
10. W. James Murdoch

Papers from Ruiming Cao's perspective

Ordered by how strongly the researchers Ruiming Cao 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Interpreting CNN knowledge via an Explanatory Graph (2017) — 256 citations
• Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning (2016) — 74 citations
• Interactively Transferring CNN Patterns for Part Localization (2017) — 16 citations
• Mining Object Parts from CNNs via Active Question-Answering (2017) — 26 citations