Yingtong Dou

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 28 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it
• Graph Learning — 30 citations into it
• Information Systems — 26 citations into it
• Safety, Robustness & Interpretability — 22 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Computer Networks and Communications — 12 citations into it
• Molecular Biology — 11 citations into it
• Signal Processing — 8 citations into it

Whom Yingtong Dou trusts overall

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

1. Thomas Kipf
2. William L. Hamilton
3. Max Welling
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Diederik P. Kingma
7. Jure Leskovec
8. Alex Krizhevsky
9. Kaiming He
10. Shebuti Rayana

Who trusts Yingtong Dou overall

1. Jianheng Tang
2. Yang Liu
3. Xiaoxiao Ma
4. Jiajin Li

Papers from Yingtong Dou's perspective

Ordered by how strongly the researchers Yingtong Dou 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. 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

• User Preference-aware Fake News Detection (2021) — 345 citations
• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (2020) — 744 citations
• Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection (2020) — 383 citations
• Adversarial Attack and Defense on Graph Data: A Survey (2018) — 389 citations