Zhong Fan

Artificial Intelligence · with András · 2020

A researcher in the Science corpus: 3 papers; trusts 49 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it
• Computer Networks and Communications — 27 citations into it
• Industrial and Manufacturing Engineering — 18 citations into it
• Signal Processing — 13 citations into it
• Control and Systems Engineering — 12 citations into it
• Federated & Distributed Learning — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Information Systems — 6 citations into it

Whom Zhong Fan trusts overall

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

1. Fei Tao
2. Qinglin Qi
3. Yann LeCun
4. H. Brendan McMahan
5. Virginia Smith
6. Alem Čolaković
7. Pramila P. Shinde
8. Yoshua Bengio
9. Daniel Povey
10. Geoffrey E. Hinton

Who trusts Zhong Fan overall

1. Omar Abdel Wahab

Papers from Zhong Fan's perspective

Ordered by how strongly the researchers Zhong Fan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Federated learning with hierarchical clustering of local updates to improve training on non-IID data (2020) — 767 citations
• Anomaly Detection for IoT Time-Series Data: A Survey (2020) — 650 citations
• Digital Twin: Enabling Technologies, Challenges and Open Research (2020) — 1,923 citations