跨损失的缩放行为在识别透相位过渡的识别中
Huiyao Li1, Yu Zhao1, Bo Yang1
1Data Science Research Center, <a href="https://ror.org/00xyeez13">Kunming University of Science and Technology</a>, 727 South Jingming Road, Kunming 650500, China and Faculty of Science, <a href="https://ror.org/00xyeez13">Kunming University of Science and Technology</a>, 727 South Jingming Road, Kunming 650500, China.
Physical review. E
|December 18, 2024
概括
这项研究揭示了交叉损失在深度学习模型中表现出扩展行为. 标签和聚合方法对结果产生重大影响,SAGPooling-Mean证明对分析关键现象有效.
科学领域:
- 统计物理 统计物理
- 机器学习 机器学习
- 复杂的网络 复杂的网络
背景情况:
- 交叉损失是对分类模型性能的一种标准度量.
- 深度神经网络越来越多地应用于复杂的物理系统.
- 透理论模拟了各种系统中的关键现象.
研究的目的:
- 为了研究深度神经网络中交叉损失的缩放行为,应用于透模型.
- 评估不同神经网络架构 (CNN,GCN) 的影响,并将透分析的方法汇集起来.
- 确定标记策略对临界点和指数估计的准确性的影响.
主要方法:
- 使用各种聚合策略的卷积神经网络 (CNN) 和图形卷积网络 (GCN).
- 在正方形格子和埃尔多斯-雷尼 (ER) 网络上研究了场地透.
- 采用两种标签方法:跨集群和基于关键点的标签.
主要成果:
- 在透模型中分析深度神经网络时,交叉损失表现出明显的缩放行为.
- 缩放指数 (ψ/ν) 对标签和聚合方法的选择敏感.
- 基于关键点的标签准确地捕获有限系统中的关键行为,类似于无限系统中的跨集群标签.
结论:
- 深度学习模型,特别是GCN,可以有效地探测透阶段过渡和关键现象.
- SAGPooling-Mean方法在分析网格和网络透的交叉损失缩放方面表现出有效性.
- 仔细选择标签和聚合策略对于使用机器学习对关键指数的可靠分析至关重要.
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