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pro vyhledávání: '"Minami, Shunya"'
Autor:
Minami, Shunya, Hayashi, Yoshihiro, Wu, Stephen, Fukumizu, Kenji, Sugisawa, Hiroki, Ishii, Masashi, Kuwajima, Isao, Shiratori, Kazuya, Yoshida, Ryo
To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such as molecular dynamics simulations. Previous studies have shown that fi
Externí odkaz:
http://arxiv.org/abs/2408.04042
Publikováno v:
NeurIPS 2023
Supervised transfer learning has received considerable attention due to its potential to boost the predictive power of machine learning in scenarios where data are scarce. Generally, a given set of source models and a dataset from a target domain are
Externí odkaz:
http://arxiv.org/abs/2210.09745
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated
Externí odkaz:
http://arxiv.org/abs/2006.13228
Akademický článek
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Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:8992-8999
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated