Hong-You Chen

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 155 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it
• Optimization & Training — 24 citations into it
• Federated & Distributed Learning — 23 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Efficiency & Compression — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Computer Science Applications — 8 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom Hong-You Chen trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. Sergey Ioffe
8. Leo Breiman
9. Christian Szegedy
10. Thomas G. Dietterich

Who trusts Hong-You Chen overall

1. Lin Zhang
2. Li Shen
3. Xuefei Yin

Papers from Hong-You Chen's perspective

Ordered by how strongly the researchers Hong-You 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On Bridging Generic and Personalized Federated Learning for Image Classification (2022) — 345 citations
• FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning (2020) — 342 citations