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From distributed machine learning to federated learning: a survey

Knowledge and information systems, 2022-04, Vol.64 (4), p.885-917

2022

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CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity

BMC bioinformatics, 2023-10, Vol.24 (1), p.1-378, Article 378

2023

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Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

Journal of medical Internet research, 2017-03, Vol.19 (3), p.e62-e62

2017

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effSense: A Novel Mobile Crowd-Sensing Framework for Energy-Efficient and Cost-Effective Data Uploading

IEEE transactions on systems, man, and cybernetics. Systems, 2015-12, Vol.45 (12), p.1549-1563

2015

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Improving covariance-regularized discriminant analysis for EHR-based predictive analytics of diseases

Applied intelligence (Dordrecht, Netherlands), 2021, Vol.51 (1), p.377-395

2021

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MODES: model-based optimization on distributed embedded systems

Machine learning, 2021-06, Vol.110 (6), p.1527-1547

2021

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Flexible energy load identification in intelligent manufacturing for demand response using a neural network integrated particle swarm optimization

Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science, 2022-02, Vol.236 (4), p.1943-1959

2022

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BASA: building mobile Ad-Hoc social networks on top of android

IEEE network, 2014-01, Vol.28 (1), p.4-9

2014

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Mobility prediction in telecom cloud using mobile calls

IEEE wireless communications, 2014-02, Vol.21 (1), p.26-32

2014

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OGM: Online gaussian graphical models on the fly

Applied intelligence (Dordrecht, Netherlands), 2022-02, Vol.52 (3), p.3103-3117

2022

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Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget

IEEE internet of things journal, 2016-12, Vol.3 (6), p.1395-1405

2016

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Generalising combinatorial discriminant analysis through conditioning truncated Rayleigh flow

Knowledge and information systems, 2021-08, Vol.63 (8), p.2189-2208

2021

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A Simple yet Effective Framework for Active Learning to Rank

International journal of automation and computing, 2024-02, Vol.21 (1), p.169-183

2024

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Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer

Machine learning: science and technology, 2024-03, Vol.5 (1), p.15039

2024

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EdgeSense: Edge-Mediated Spatial- Temporal Crowdsensing

IEEE access, 2019-01, Vol.7, p.95122-95131

2019

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ecoSense: Minimize Participants' Total 3G Data Cost in Mobile Crowdsensing Using Opportunistic Relays

IEEE transactions on systems, man, and cybernetics. Systems, 2017-06, Vol.47 (6), p.965-978

2017

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Mobile Sensing in the COVID-19 Era: A Review

Health data science, 2022, Vol.2022, p.9830476-9830476

2022

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Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond

Knowledge and information systems, 2022-12, Vol.64 (12), p.3197-3234

2022

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Semi-Supervised Air Quality Forecasting via Self-Supervised Hierarchical Graph Neural Network

IEEE transactions on knowledge and data engineering, 2023-05, Vol.35 (5), p.5230-5243

2023

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Moving second generation biofuel manufacturing forward: Investigating economic viability and environmental sustainability considering two strategies for supply chain restructuring

Applied energy, 2019-05, Vol.242, p.1467-1496

2019

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AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow

Machine learning, 2021-08, Vol.110 (8), p.2131-2150

2021

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Flood recovery and property acquisition in Cedar Rapids, Iowa

Natural hazards (Dordrecht), 2016-02, Vol.80 (3), p.2055-2079

2016

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Cross-model consensus of explanations and beyond for image classification models: an empirical study

Machine learning, 2023-05, Vol.112 (5), p.1627-1662

2023

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CrowdTasker: Maximizing coverage quality in Piggyback Crowdsensing under budget constraint

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2015, p.55-62

2015

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G-LIME: Statistical learning for local interpretations of deep neural networks using global priors

Artificial intelligence, 2023-01, Vol.314, p.103823, Article 103823

2023

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From distributed machine learning to federated learning: a survey

CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

effSense: A Novel Mobile Crowd-Sensing Framework for Energy-Efficient and Cost-Effective Data Uploading

Improving covariance-regularized discriminant analysis for EHR-based predictive analytics of diseases

MODES: model-based optimization on distributed embedded systems

Flexible energy load identification in intelligent manufacturing for demand response using a neural network integrated particle swarm optimization

BASA: building mobile Ad-Hoc social networks on top of android

Mobility prediction in telecom cloud using mobile calls

OGM: Online gaussian graphical models on the fly

Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget

Generalising combinatorial discriminant analysis through conditioning truncated Rayleigh flow

A Simple yet Effective Framework for Active Learning to Rank

Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer

EdgeSense: Edge-Mediated Spatial- Temporal Crowdsensing

ecoSense: Minimize Participants' Total 3G Data Cost in Mobile Crowdsensing Using Opportunistic Relays

Mobile Sensing in the COVID-19 Era: A Review

Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond

Semi-Supervised Air Quality Forecasting via Self-Supervised Hierarchical Graph Neural Network

Moving second generation biofuel manufacturing forward: Investigating economic viability and environmental sustainability considering two strategies for supply chain restructuring

AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow

Flood recovery and property acquisition in Cedar Rapids, Iowa

Cross-model consensus of explanations and beyond for image classification models: an empirical study

CrowdTasker: Maximizing coverage quality in Piggyback Crowdsensing under budget constraint

G-LIME: Statistical learning for local interpretations of deep neural networks using global priors

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