| Autor: |
Xiaohua Xing, Yuqi Ren, Die Zou, Qiankun Zhang, Bingxuan Mao, Jianquan Yao, Deyi Xiong, Liang Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
APL Machine Learning, Vol 2, Iss 2, Pp 026118-026118-8 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2770-9019 |
| DOI: |
10.1063/5.0203931 |
| Popis: |
Recently, the rapid progress of deep learning techniques has brought unprecedented transformations and innovations across various fields. While neural network-based approaches can effectively encode data and detect underlying patterns of features, the diverse formats and compositions of data in different fields pose challenges in effectively utilizing these data, especially for certain research fields in the early stages of integrating deep learning. Therefore, it is crucial to find more efficient ways to utilize existing datasets. Here, we demonstrate that the predictive accuracy of the network can be improved dramatically by simply adding supplementary multi-frequency inputs to the existing dataset in the target spectrum predicting process. This design methodology paves the way for interdisciplinary research and applications at the interface of deep learning and other fields, such as photonics, composite material design, and biological medicine. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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