Pan Li

Artificial Intelligence · Neural Information Processing Systems · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 201 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 216 citations into it; #25,526 of 35,345 by trust here, #21,316 by citations
• Graph Learning — 121 citations into it
• Statistical and Nonlinear Physics — 88 citations into it
• Computer Vision and Pattern Recognition — 58 citations into it
• Representation & Self-Supervised Learning — 55 citations into it
• Molecular Biology — 50 citations into it
• Materials Chemistry — 35 citations into it
• Generative Models — 28 citations into it

Whom Pan Li trusts overall

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

1. Thomas Kipf
2. Geoffrey E. Hinton
3. Max Welling
4. Yoshua Bengio
5. Diederik P. Kingma
6. William L. Hamilton
7. Jure Leskovec
8. Alex Krizhevsky
9. Yann LeCun
10. Ilya Sutskever

Who trusts Pan Li overall

1. Jing Huang
2. Dexiong Chen
3. Vijay Prakash Dwivedi
4. Ladislav Rampášek
5. Yingmin Wu
6. Xiyuan Wang
7. Jie Yang
8. Susheel Suresh
9. Leslie O’Bray
10. Siqi Miao

Papers from Pan Li's perspective

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

Their papers

• Adaptive Universal Generalized PageRank Graph Neural Network (2020) — 1,039 citations
• Adversarial Graph Augmentation to Improve Graph Contrastive Learning (2021) — 456 citations
• Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism (2022) — 302 citations
• Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (2021) — 355 citations
• Graph Information Bottleneck (2020) — 350 citations
• Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning (2020) — 382 citations
• Conditional Structure Generation through Graph Variational Generative Adversarial Nets (2019) — 114 citations