Jun Zhou

Artificial Intelligence · Industrial Conference on Data Mining · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 30 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #35,161 of 35,345 by trust here, #15,236 by citations
• Information Systems — 55 citations into it
• Graph Learning — 46 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Management Science and Operations Research — 20 citations into it
• Optimization & Training — 15 citations into it
• Recommender Systems — 14 citations into it

Whom Jun Zhou trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Ruslan Salakhutdinov
6. Jimmy Ba
7. William L. Hamilton
8. Alex Krizhevsky
9. Max Welling
10. Thomas Kipf

Who trusts Jun Zhou overall

1. A. Ozbayoglu

Papers from Jun Zhou's perspective

Ordered by how strongly the researchers Jun Zhou 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

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
• Bandit Samplers for Training Graph Neural Networks (2020) — 54 citations
• A Semi-Supervised Graph Attentive Network for Financial Fraud Detection (2019) — 468 citations
• Heterogeneous Graph Neural Networks for Malicious Account Detection (2018) — 415 citations
• POSTER: Practical Fraud Transaction Prediction (2017)