Fu-lai Chung

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 29 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Graph Learning — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Fu-lai Chung trusts overall

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

1. Jiliang Tang
2. Jure Leskovec
3. Tomas Mikolov
4. Aditya Grover
5. Quan Zheng
6. Daniel Klein
7. Yoshua Bengio
8. Bryan Perozzi
9. J´erˆome Kunegis
10. Geoffrey E. Hinton

Papers from Fu-lai Chung's perspective

Ordered by how strongly the researchers Fu-lai Chung trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• Deep Network Embedding for Graph Representation Learning in Signed Networks (2019) — 100 citations