Xi Song

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 1,350 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 221 citations into it; #18,487 of 21,575 by trust here, #15,162 by citations
• Artificial Intelligence — 157 citations into it; #30,161 of 35,345 by trust here, #23,077 by citations
• Model Architectures — 56 citations into it
• Safety, Robustness & Interpretability — 39 citations into it
• Natural Language Processing — 23 citations into it
• Optimization & Training — 23 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Generative Models — 12 citations into it

Whom Xi Song trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Christian Szegedy
7. Shaoqing Ren
8. Yann LeCun
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Xi Song overall

1. Quanshi Zhang
2. Gabrielle Ras

Papers from Xi Song's perspective

Ordered by how strongly the researchers Xi Song 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Towards Interpretable Object Detection by Unfolding Latent Structures (2019) — 28 citations
• AOGNets: Deep AND-OR Grammar Networks for Visual Recognition (2017) — 10 citations
• AOGNets: Compositional Grammatical Architectures for Deep Learning (2017) — 26 citations
• Towards Interpretable R-CNN by Unfolding Latent Structures (2017) — 20 citations
• Interpretable R-CNN (2017) — 18 citations