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转移学习用于基于光子延迟的储计算,以补偿参数漂移
Ian Bauwens1, Krishan Harkhoe1, Peter Bienstman2
1Applied Physics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.
Nanophotonics (Berlin, Germany)
|December 5, 2024
概括
转移学习降低了面临参数漂移的光子储库计算系统的再培训成本. 这种方法还降低了对后续任务的输入数据需求,节省了时间和能量.
科学领域:
- 物理 物理学 物理
- 计算机科学 计算机科学
- 光学工程是指光学工程.
背景情况:
- 光子储计算 (PRC) 在复杂的问题解决方面表现出色.
- 由于实验设置中的参数漂移,PRC系统的重新训练是资源密集的.
研究的目的:
- 为了研究转移学习作为一个解决方案,参数漂移在中国.
- 为了减少重新训练PRC系统所需的时间,精力和数据.
主要方法:
- 使用半导体激光器对基于延迟的PRC系统进行数值研究.
- 转移学习的应用,以减轻参数波动和减少再培训需求.
主要成果:
- 转移学习有效地弥补了中国的参数漂移.
- 当使用转移学习时,需要减少输入样本来训练二次任务.
结论:
- 转移学习是保持中国人民共和国表现的可行和有效的技术.
- 这种方法显著降低了与PRC系统维护和调整相关的计算和数据开销.
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