Xilai Li

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 1,040 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 186 citations into it; #7,542 of 21,575 by trust here, #4,245 by citations
• Artificial Intelligence — 146 citations into it; #11,042 of 35,345 by trust here, #5,749 by citations
• Model Architectures — 55 citations into it
• Safety, Robustness & Interpretability — 30 citations into it
• Optimization & Training — 25 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Representation & Self-Supervised Learning — 17 citations into it; #1,385 of 3,145 by trust here, #856 by citations
• Natural Language Processing — 15 citations into it

Whom Xilai Li trusts overall

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

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

Who trusts Xilai Li overall

1. Jorge Armando Mendez Mendez
2. Arthur Douillard
3. Frederik Benzing
4. Quang Pham
5. Pravendra Singh
6. Divyam Madaan
7. Da-Wei Zhou
8. Jaehong Yoon
9. Miao Zhang
10. Seyed Iman Mirzadeh

Papers from Xilai Li's perspective

Ordered by how strongly the researchers Xilai Li 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. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (2019) — 532 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