Dianbo Liu

Artificial Intelligence · with Miller · 2018–2020

A researcher in the Science corpus: 9 papers; trusts 117 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #5,498 of 35,345 by trust here, #9,170 by citations
• Optimization & Training — 24 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Federated & Distributed Learning — 20 citations into it; #82 of 319 by trust here, #153 by citations
• Efficiency & Compression — 18 citations into it
• Epidemiology — 16 citations into it
• Health Information Management — 14 citations into it; #10 of 344 by trust here, #58 by citations
• Molecular Biology — 12 citations into it; #11,880 of 17,773 by trust here, #15,203 by citations

Whom Dianbo Liu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Jakub Konecný
4. Radford M. Neal
5. H. Brendan McMahan
6. Alex Krizhevsky
7. Alistair E. W. Johnson
8. Yoshua Bengio
9. Kaiming He
10. Yann LeCun

Who trusts Dianbo Liu overall

1. Li Huang
2. Dinh C. Nguyen
3. Rodolfo Stoffel Antunes
4. Li Li
5. Yifeng Yin
6. Cristiano André da Costa
7. Quoc-Viet Pham
8. Yuxi Fan
9. Dmitriy Dligach
10. Zeng Fu

Papers from Dianbo Liu's perspective

Ordered by how strongly the researchers Dianbo Liu 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. Long Short-Term Memory
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. 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

• Federated pretraining and fine tuning of BERT using clinical notes from multiple silos (2020) — 38 citations
• Two-stage Federated Phenotyping and Patient Representation Learning (2019) — 92 citations
• Patients with cancer appear more vulnerable to SARS-COV-2: a multi-center study during the COVID-19 outbreak (2020) — 1,428 citations
• Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records (2019) — 424 citations
• Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence (2019) — 10 citations
• Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden (2018) — 7 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
• FADL: Federated-Autonomous Deep Learning for Distributed Electronic Health Record (2018) — 68 citations