Li Huang

Artificial Intelligence · with Liu · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #9,995 of 35,345 by trust here, #12,580 by citations
• Optimization & Training — 16 citations into it
• Federated & Distributed Learning — 11 citations into it; #75 of 319 by trust here, #153 by citations
• Computational Mechanics — 8 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it
• Health Information Management — 5 citations into it; #50 of 344 by trust here, #84 by citations

Whom Li Huang trusts overall

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

1. Dianbo Liu
2. H. Brendan McMahan
3. Peter Richtárik
4. Diederik P. Kingma
5. Junyuan Xie
6. Jakub Konecný
7. Tom J. Pollard
8. L. Eon Bottou
9. Martin Takác
10. Ross B. Girshick

Who trusts Li Huang overall

1. Li Li
2. Dianbo Liu
3. Rodolfo Stoffel Antunes
4. Yuxi Fan
5. Sicong Che
6. Jie Xu
7. Dmitriy Dligach
8. Dinh C. Nguyen
9. Cristiano André da Costa
10. Haibo Yang

Papers from Li Huang's perspective

Ordered by how strongly the researchers Li Huang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records (2019) — 424 citations
• LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data (2018) — 73 citations
• LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data (2018) — 150 citations