Dianbo Liu

发表

Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

Alessandro Vespignani, Mauricio Santillana, Dianbo Liu, 2020, Journal of medical Internet research.

Two-stage Federated Phenotyping and Patient Representation Learning pdf

Dmitriy Dligach, Dianbo Liu, Timothy A. Miller, 2019, BioNLP@ACL.

DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain

Andrew Shea, Rosalind W. Picard, Dianbo Liu, 2017, AffComp@IJCAI.

High Performance Computing on Flat FHIR Files Created with the New SMART/HL7 Bulk Data Access Standard

Dianbo Liu, Dan Gottlieb, Kenneth D Mandl, 2019, AMIA.

Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records pdf

Dianbo Liu, Jianfei Cui, 2020, ArXiv.

Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records

Li Huang, Dianbo Liu, Dianbo Liu, 2019, J. Biomed. Informatics.

Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning pdf

Dianbo Liu, Hui Liu, Rulin Shao, 2019, ArXiv.

Border Effect of Complex Network: An analysis on the cooperation network of movie stars across different regions pdf

Chen Chen, Wen Zhou, Dong Zhang, 2018, ArXiv.

Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence pdf

Kenneth D. Mandl, Dianbo Liu, Timothy A Miller, 2019, ArXiv.

Using artificial neural network condensation to facilitate adaptation of machine learning in medical settings by reducing computational burden (Preprint)

Dianbo Liu, Ming Zheng, Nestor Sepulveda, 2020 .

Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving pdf

Dianbo Liu, Hongyu He, Hui Liu, 2019, ArXiv.

Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation.

Hui Liu, Dianbo Liu, Hongyu He, 2019, JMIR formative research.

Genie: A Secure, Transparent Sharing and Services Platform for Genetic and Health Data pdf

Thomas Hardjono, Manolis Kellis, ZhiZhuo Zhang, 2018, ArXiv.

FADL: Federated-Autonomous Deep Learning for Distributed Electronic Health Record pdf

Kenneth D. Mandl, Dianbo Liu, Raheel Sayeed, 2018, ArXiv.

PrimeDesign software for rapid and simplified design of prime editing guide RNAs

Jonathan Y. Hsu, Lam Kc, Luca Pinello, 2020 .

LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data pdf

Hao Deng, Dianbo Liu, Li Huang, 2018, ArXiv.

Leyu Dai, Dianbo Liu, He Zhu, 2020, ArXiv.

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

Dianbo Liu, Leonardo Clemente, Matteo Chinazzi, 2020, ArXiv.

Movies Network as the Indicator of Globalization pdf

Chen Chen, Dianbo Liu, Zilu Yu, 2018, ArXiv.

FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network pdf

Dianbo Liu, He Zhu, 2020, ArXiv.

Modelling biological networks : topology, dynamics and generation

Dianbo Liu, 2017 .

Balance of thrones: a network study on 'Game of Thrones' pdf

Dianbo Liu, Luca Albergante, L. Albergante, 2017, ArXiv.

Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden pdf

Ming Zheng, Dianbo Liu, Néstor Sepúlveda, 2018, ArXiv.

Federated pretraining and fine tuning of BERT using clinical notes from multiple silos pdf

Tim Miller, Dianbo Liu, Timothy Miller, 2020, ArXiv.

LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

Li Huang, Zeng Fu, Dianbo Liu, 2018, PloS one.

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records pdf

Dianbo Liu, Tianyi Zhang, Shirui Zhang, 2021, 2022 IEEE International Conference on Big Data (Big Data).

FeARH: Federated machine learning with anonymous random hybridization on electronic medical records

Dianbo Liu, He Zhu, Ziwei Chen, 2021, J. Biomed. Informatics.

Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

Alessandro Vespignani, Mauricio Santillana, Dianbo Liu, 2020, Journal of medical Internet research.