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hBert + BiasCorp -- Fighting Racism on the Web

Apr 06, 2021
Olawale Onabola, Zhuang Ma, Yang Xie, Benjamin Akera, Abdulrahman Ibraheem, Jia Xue, Dianbo Liu, Yoshua Bengio

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

Dec 10, 2020
He Zhu, Dianbo Liu

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Patient similarity: methods and applications

Dec 01, 2020
Leyu Dai, He Zhu, Dianbo Liu

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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

Apr 08, 2020
Dianbo Liu, Leonardo Clemente, Canelle Poirier, Xiyu Ding, Matteo Chinazzi, Jessica T Davis, Alessandro Vespignani, Mauricio Santillana

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

Feb 20, 2020
Dianbo Liu, Tim Miller

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

Dec 25, 2019
Jianfei Cui, Dianbo Liu

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Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving

Nov 15, 2019
Rulin Shao, Hongyu He, Hui Liu, Dianbo Liu

* 6 pages including references, 2 figures, Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract. arXiv admin note: substantial text overlap with arXiv:1910.02115 

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

Oct 04, 2019
Rulin Shao, Hui Liu, Dianbo Liu

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

Oct 04, 2019
Dianbo Liu, Timothy A Miller, Kenneth D. Mandl

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Two-stage Federated Phenotyping and Patient Representation Learning

Aug 14, 2019
Dianbo Liu, Dmitriy Dligach, Timothy Miller

* 9 pages; Proceedings of the 18th BioNLP Workshop and Shared Task 

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

Mar 22, 2019
Li Huang, Dianbo Liu

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

Dec 23, 2018
Dianbo Liu, Nestor Sepulveda, Ming Zheng

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

Dec 03, 2018
Dianbo Liu, Timothy Miller, Raheel Sayeed, Kenneth D. Mandl

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 
* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200 

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

Nov 30, 2018
Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, Dianbo Liu

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

Aug 09, 2017
Dianbo Liu, Fengjiao Peng, Andrew Shea, Ognjen, Rudovic, Rosalind Picard

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