Xiaolin Li

Artificial Intelligence · with Zhu · 2017–2021

A researcher in the Science corpus: 8 papers; trusts 636 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 245 citations into it; #20,591 of 35,345 by trust here, #8,439 by citations
• Computer Vision and Pattern Recognition — 126 citations into it
• Safety, Robustness & Interpretability — 87 citations into it; #1,681 of 2,251 by trust here, #876 by citations
• Natural Language Processing — 86 citations into it
• Signal Processing — 42 citations into it
• Model Architectures — 37 citations into it
• Computer Networks and Communications — 28 citations into it; #7,953 of 9,886 by trust here, #9,214 by citations
• Optimization & Training — 23 citations into it

Whom Xiaolin Li trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Ilya Sutskever
5. Diederik P. Kingma
6. Yann LeCun
7. Percy Liang
8. Kaiming He
9. Sepp Hochreiter
10. Christian Szegedy

Who trusts Xiaolin Li overall

1. Edward Chou
2. Hongyu Liu

Papers from Xiaolin Li's perspective

Ordered by how strongly the researchers Xiaolin Li 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Adversarial Examples: Attacks and Defenses for Deep Learning (2017) — 1,830 citations
• Single Shot Text Detector with Regional Attention (2017) — 315 citations
• Distilled One-Shot Federated Learning (2020) — 192 citations
• Server Averaging for Federated Learning (2021) — 4 citations
• Statistical learning for semantic parsing: A survey (2019) — 10 citations
• Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring (2019) — 68 citations
• Supplementary Information for Predicting the clinical impact of human mutation with deep neural networks (2018)
• DeepDefense: Identifying DDoS Attack via Deep Learning (2017) — 341 citations