Chaochao Chen

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 33 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #34,282 of 35,345 by trust here, #14,718 by citations
• Information Systems — 54 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Graph Learning — 25 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Recommender Systems — 15 citations into it
• Optimization & Training — 13 citations into it
• Statistical and Nonlinear Physics — 9 citations into it

Whom Chaochao Chen trusts overall

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

1. Steffen Rendle
2. Ruslan Salakhutdinov
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Kaiming He
7. John F. Canny
8. Alex Krizhevsky
9. Prastudy Fauzi
10. Yann LeCun

Who trusts Chaochao Chen overall

1. Ziqi Liu
2. Yingtong Dou
3. Hongwei Wang
4. Zhiwei Liu
5. Huan Zhao

Papers from Chaochao Chen's perspective

Ordered by how strongly the researchers Chaochao 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• GeniePath: Graph Neural Networks with Adaptive Receptive Paths (2018) — 350 citations
• Practical Privacy Preserving POI Recommendation (2020) — 77 citations
• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021) — 301 citations
• A Deep Framework for Cross-Domain and Cross-System Recommendations (2018) — 177 citations
• Heterogeneous Graph Neural Networks for Malicious Account Detection (2018) — 415 citations