Tong Chen

Artificial Intelligence · IEEE Transactions on Knowledge and Data Engineering · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 142 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it
• Information Systems — 94 citations into it
• Computer Vision and Pattern Recognition — 75 citations into it
• Representation & Self-Supervised Learning — 43 citations into it
• Natural Language Processing — 16 citations into it
• Model Architectures — 15 citations into it
• Recommender Systems — 12 citations into it
• Graph Learning — 10 citations into it

Whom Tong Chen trusts overall

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

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

Who trusts Tong Chen overall

1. Jing Ma
2. Dhruv Khattar
3. Junliang Yu
4. Wei Gao

Papers from Tong Chen's perspective

Ordered by how strongly the researchers Tong Chen 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (2017) — 323 citations
• Self-Supervised Learning for Recommender Systems: A Survey (2022) — 438 citations
• XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (2022) — 401 citations
• Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation (2021) — 990 citations
• Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation (2020) — 428 citations