Shirui Pan

Artificial Intelligence · IEEE Transactions on Knowledge and Data Engineering · 2014–2023

A researcher in the Science corpus: 25 papers; trusts 1,030 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 992 citations into it; #5,298 of 35,345 by trust here, #1,845 by citations
• Computer Vision and Pattern Recognition — 395 citations into it; #19,707 of 21,575 by trust here, #2,749 by citations
• Graph Learning — 286 citations into it; #674 of 693 by trust here, #101 by citations
• Statistical and Nonlinear Physics — 209 citations into it; #3,912 of 10,127 by trust here, #1,698 by citations
• Representation & Self-Supervised Learning — 175 citations into it
• Natural Language Processing — 159 citations into it; #4,684 of 5,688 by trust here, #2,172 by citations
• Molecular Biology — 115 citations into it; #17,303 of 17,773 by trust here, #10,347 by citations
• Signal Processing — 111 citations into it

Whom Shirui Pan trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Tomas Mikolov
9. Max Welling
10. Alex Graves

Who trusts Shirui Pan overall

1. Yixin Liu
2. Zezhi Shao
3. Zonghan Wu
4. Guodong Long
5. Yaochen Xie
6. Ming Jin
7. Ganqu Cui
8. Chun Wang
9. Aleksandar Bojchevski
10. Weiwei Jiang

Papers from Shirui Pan's perspective

Ordered by how strongly the researchers Shirui Pan 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Comprehensive Survey on Graph Neural Networks (2019) — 11,551 citations
• DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding (2017) — 787 citations
• iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients (2021) — 85 citations
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (2020) — 2,163 citations
• A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020) — 2,800 citations
• MGAE: Marginalized Graph Autoencoder for Graph Clustering (2017) — 521 citations
• Adversarially Regularized Graph Autoencoder (2018) — 489 citations
• Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications (2019) — 228 citations
• Unifying Large Language Models and Knowledge Graphs: A Roadmap (2023) — 1,606 citations
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023) — 498 citations
• Graph Neural Networks for Graphs With Heterophily: A Survey (2022) — 315 citations
• Deep Learning for Time Series Anomaly Detection: A Survey (2022) — 636 citations
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021) — 488 citations
• Graph Learning: A Survey (2021) — 510 citations
• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning (2021) — 383 citations
• Graph Self-Supervised Learning: A Survey (2021) — 758 citations
• One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting (2020) — 81 citations
• Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity Maximization (2020) — 89 citations
• Attributed Graph Clustering: A Deep Attentional Embedding Approach (2019) — 645 citations
• Learning Graph Embedding With Adversarial Training Methods (2019) — 347 citations