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Proceedings of the Third (2016) ACM Conference on Learning @ Scale, 2016
The rising number of Massive Open Online Courses (MOOCs) enable people to advance their knowledge and competencies in a wide range of fields. Learning though is only the first step, the transfer of the taught concepts into practice is equally important and often neglected in the investigation of MOOCs.
Guanliang Chen +3 more
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The rising number of Massive Open Online Courses (MOOCs) enable people to advance their knowledge and competencies in a wide range of fields. Learning though is only the first step, the transfer of the taught concepts into practice is equally important and often neglected in the investigation of MOOCs.
Guanliang Chen +3 more
openaire +1 more source
2010
Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned. While most machine learning algorithms are designed to address single tasks, the development of algorithms that facilitate transfer learning is a topic of ongoing interest in the machine-learning community.
Lisa Torrey, Jude Shavlik
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Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned. While most machine learning algorithms are designed to address single tasks, the development of algorithms that facilitate transfer learning is a topic of ongoing interest in the machine-learning community.
Lisa Torrey, Jude Shavlik
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Technology Transfer and Learning
Technology Analysis & Strategic Management, 2002Despite the fact that international technology transfer has been widely studied its management still encounters many difficulties. To fully understand the issues that are relevant to the process of transferring production technology, it is necessary to determine the important factors that influence this process.
Steenhuis, Harm-Jan, de Bruijn, Erik J.
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Interhemispheric transfer of learning
Life Sciences, 1965Abstract Interhemispheric transfer of a simple discrimination was studied by means of spreading cortical depression. Following a single trial with both hemispheres functional transfer to the untrained hemisphere did not occur if SD was elicited in either hemisphere fifteen seconds after the transfer trial.
O S, RAY, G, EMLEY
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Incomplete Multisource Transfer Learning
IEEE Transactions on Neural Networks and Learning Systems, 2018Transfer learning is generally exploited to adapt well-established source knowledge for learning tasks in weakly labeled or unlabeled target domain. Nowadays, it is common to see multiple sources available for knowledge transfer, each of which, however, may not include complete classes information of the target domain.
Zhengming Ding, Ming Shao, Yun Fu
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Transfer Ordinal Label Learning
IEEE Transactions on Neural Networks and Learning Systems, 2013Designing a classifier in the absence of labeled data is becoming a common encounter as the acquisition of informative labels is often difficult or expensive, particularly on new uncharted target domains. The feasibility of attaining a reliable classifier for the task of interest is embarked by some in transfer learning, where label information from ...
Chun-Wei, Seah +2 more
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Learning Transfers via Transfer Learning
2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS), 2021Md Arifuzzaman, Engin Arslan
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A Survey on Deep Transfer Learning
International Conference on Artificial Neural Networks, 2018As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains.
Chuanqi Tan +5 more
semanticscholar +1 more source
Big Transfer (BiT): General Visual Representation Learning
European Conference on Computer Vision, 2019Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision.
Alexander Kolesnikov +6 more
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