Chuan Shi

Artificial Intelligence · Knowledge Discovery and Data Mining · 2013–2021

A researcher in the Science corpus: 15 papers; trusts 320 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 292 citations into it; #11,372 of 35,345 by trust here, #5,252 by citations
• Statistical and Nonlinear Physics — 119 citations into it; #9,607 of 10,127 by trust here, #1,755 by citations
• Graph Learning — 115 citations into it
• Computer Vision and Pattern Recognition — 109 citations into it
• Information Systems — 105 citations into it; #2,662 of 8,771 by trust here, #1,413 by citations
• Representation & Self-Supervised Learning — 76 citations into it
• Molecular Biology — 36 citations into it
• Recommender Systems — 34 citations into it

Whom Chuan Shi trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ruslan Salakhutdinov
5. Alex Krizhevsky
6. Kaiming He
7. Yehuda Koren
8. Yann LeCun
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Chuan Shi overall

1. Xiao Wang
2. Binbin Hu
3. Yuping Zhang
4. Xinyu Fu
5. Qingyu Guo
6. Shiwen Wu
7. Aminu Da’u
8. Walid Krichene
9. Shaohua Fan
10. Seyed Mehran Kazemi

Papers from Chuan Shi's perspective

Ordered by how strongly the researchers Chuan Shi 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Beyond Low-frequency Information in Graph Convolutional Networks (2021) — 808 citations
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks (2020) — 616 citations
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020) — 454 citations
• Heterogeneous Graph Structure Learning for Graph Neural Networks (2021) — 319 citations
• Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning (2021) — 542 citations
• Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation (2020) — 307 citations
• Structural Deep Clustering Network (2020) — 684 citations
• Heterogeneous Graph Attention Network (2019) — 3,241 citations
• Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation (2019) — 389 citations
• Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification (2019) — 363 citations
• Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model (2018) — 706 citations
• Heterogeneous Information Network Embedding for Recommendation (2017) — 1,030 citations
• Representation learning via Dual-Autoencoder for recommendation (2017) — 107 citations
• A Fast Distributed Stochastic Gradient Descent Algorithm for Matrix Factorization (2014) — 25 citations
• HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks (2013) — 329 citations