Improving LBFGS Optimizer in PyTorch: Knowledge Transfer from Radio Interferometric Calibration to Machine Learning

2018 IEEE 14th International Conference on e-Science (e-Science) ◽

10.1109/escience.2018.00112 ◽

2018 ◽

Cited By ~ 3

Author(s):

Sarod Yatawatta ◽

Hanno Spreeuw ◽

Faruk Diblen

Keyword(s):

Machine Learning ◽

Knowledge Transfer

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Machine Learning Based Localization of LoRaWAN Devices via Inter-Technology Knowledge Transfer

2020 IEEE International Conference on Communications Workshops (ICC Workshops) ◽

10.1109/iccworkshops49005.2020.9145033 ◽

2020 ◽

Author(s):

Andrea Pimpinella ◽

Alessandro E. C. Redondi ◽

Monica Nicoli ◽

Matteo Cesana

Keyword(s):

Machine Learning ◽

Knowledge Transfer ◽

Technology Knowledge

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Knowledge Transfer in Software Companies Based on Machine Learning

Advances in Intelligent Systems and Computing - Trends and Applications in Software Engineering ◽

10.1007/978-3-030-33547-2_11 ◽

2019 ◽

pp. 131-140

Author(s):

Jose Luis Jurado ◽

Alexandra CastañoTrochez ◽

Hugo Ordoñez ◽

Armando Ordoñez

Keyword(s):

Machine Learning ◽

Knowledge Transfer ◽

Software Companies

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Machine learning for knowledge transfer across multiple metals additive manufacturing printers

Additive Manufacturing ◽

10.1016/j.addma.2021.101877 ◽

2021 ◽

Vol 39 ◽

pp. 101877 ◽

Cited By ~ 1

Author(s):

Sen Liu ◽

Aaron P. Stebner ◽

Branden B. Kappes ◽

Xiaoli Zhang

Keyword(s):

Machine Learning ◽

Additive Manufacturing ◽

Knowledge Transfer

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Knowledge Transfer Through Machine Learning in Aircraft Design

IEEE Computational Intelligence Magazine ◽

10.1109/mci.2017.2742781 ◽

2017 ◽

Vol 12 (4) ◽

pp. 48-60 ◽

Cited By ~ 16

Author(s):

Alan Tan Wei Min ◽

Ramon Sagarna ◽

Abhishek Gupta ◽

Yew-Soon Ong ◽

Chi Keong Goh

Keyword(s):

Machine Learning ◽

Knowledge Transfer ◽

Aircraft Design

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Machine Learning-Assisted QSAR Models on Contaminant Reactivity Toward Four Oxidants: Combining Small Data Sets and Knowledge Transfer

Environmental Science & Technology ◽

10.1021/acs.est.1c04883 ◽

2021 ◽

Author(s):

Shifa Zhong ◽

Yanping Zhang ◽

Huichun Zhang

Keyword(s):

Machine Learning ◽

Knowledge Transfer ◽

Small Data ◽

Data Sets ◽

Qsar Models ◽

Small Data Sets

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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