Bayesian multi-task learning methodology for reconstruction of structural health monitoring data:
Hua Ping Wan,Yiqing Ni +1 more
TL;DR: The reconstruction results of the structural health monitoring data show that the proposed Bayesian multi-task learning methodology affords an excellent performance, while the Bayesian single- task learning method is unreliable in certain cases; yet, the selection of covariance function has a significant impact on the reconstruction performance of the proposed methodology.
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Abstract: Reconstruction of structural health monitoring data is a challenging task, since it involves time series data forecasting especially in the case with a large block of missing data In this study, w
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References
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Gaussian Processes For Machine Learning
Tanja Hueber
- 01 Jan 2016
TL;DR: The gaussian processes for machine learning is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can get it instantly.
10K
•Proceedings Article
Advances in Neural Information Processing Systems 31
Samy Bengio,H.M. Wallach,Hugo Larochelle,K. Grauman,Nicolò Cesa-Bianchi,R. Garnett +5 more
- 01 Jan 2018
9.4K
Fast Image Recovery Using Variable Splitting and Constrained Optimization
M. Afonso,Jose M. Bioucas-Dias,Mário A. T. Figueiredo +2 more
TL;DR: A new fast algorithm is proposed for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an l2 data-fidelity term and a nonsmooth regularizer.
•Proceedings Article
Multi-task Gaussian Process Prediction
Edwin V. Bonilla,Kian Ming A. Chai,Christopher Williams +2 more
- 03 Dec 2007
TL;DR: A model that learns a shared covariance function on input-dependent features and a "free-form" covariance matrix over tasks allows for good flexibility when modelling inter-task dependencies while avoiding the need for large amounts of data for training.