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Quantum Adversarial Transfer Learning
Entropy, 2023 Adversarial transfer learning is a machine learning method that employs an adversarial training process to learn the datasets of different domains. Recently, this method has attracted attention because it can efficiently decouple the requirements of ...
Longhan Wang, Yifan Sun, Xiangdong Zhang
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Progressive Transfer Learning [PDF]
IEEE Transactions on Image Processing, 2022 10 pages, 4 figures, journel verison of our published short paper on ...
Zhengxu Yu +5 more
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Journal of Ethnic Foods, 2022 The increased frequency of extreme climate-induced natural disasters (floods, cyclones, mud slides, heat waves, droughts), attributed to climate change, is causing stress to already vulnerable livelihoods by affecting both tangible and intangible ...
Vimbainashe Prisca Dembedza +3 more
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Energies, 2021 The intermittent nature of renewable sources, such as solar and wind, leads to the need for a hybrid renewable energy system (HRES) that can provide uninterrupted and reliable energy to a remote and off-grid location with the use of a biogas generator ...
Vijay Mudgal +6 more
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Faculty and Student Attitudes about Transfer of Learning [PDF]
InSight, 2008 Transfer of learning is using previous knowledge in novel contexts. While this is a basic assumption of the educational process, students may not always perceive all the options for using what they have learned in different, novel situations.
Robin Lightner +2 more
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Health Science Reports, 2022 Background and Aims In low‐income countries where there is shortage of appropriate medical care to manage hypertension (HTN), understanding dynamics of communities' knowledge and attitude to prevent through lifestyle is crucial.
Tsegab Paulose +2 more
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A Proposal of Transfer Learning for Monthly Macroeconomic Time Series Forecast
Engineering Proceedings, 2023 Transfer learning has not been widely explored with time series. However, it could boost the application and performance of deep learning models for predicting macroeconomic time series with few observations, like monthly variables.
Martín Solís +1 more
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Electroencephalogram-Based Preference Prediction Using Deep Transfer Learning
IEEE Access, 2020 Transfer learning is an approach in machine learning where a model that was built and trained on one task is re-purposed on a second task. The success of transfer learning in computer vision has motivated its use in neuroscience. Although common in image
Mashael S. Aldayel +2 more
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Frontiers in Psychology, 2018 Introduction: Consolidation is defined as the time necessary for memory stabilization after learning. In the present study we focused on effects of interference during the first 12 consolidation minutes after learning.
Zrinka Sosic-Vasic +7 more
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Impact of intercity low-carbon technology transfer on carbon emission reduction in China:Based on the “dichotomy” of knowledge learning and technology learning [PDF]
Ziyuan Kexue, 2023 [Objective] Increasing the transfer of low-carbon technology (LCT) is the key to narrowing the gap in LCT between regions and improving the overall level of low-carbon technology of China.
SHANG Yongmin, MI Zefeng, ZHOU Can, LIN Lan
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