在神经网络之间建立等价性的转换:两个网络何时学会了相同的任务?
Tom Bertalan1, Felix Dietrich2, Ioannis G Kevrekidis3
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Chaos (Woodbury, N.Y.)
|July 18, 2024
概括
本研究引入了一种基于数据的方法,使用扩散图来确定人工神经网络之间的等价值. 这种方法有助于理解网络关系,并对转移学习产生影响.
科学领域:
- 动态系统 动态系统
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 转换对于分析动态系统至关重要,特别是在研究不稳定性和分叉时.
- 人工神经网络 (ANN) 是强大的工具,但理解不同网络实例之间的等价性是具有挑战性的.
研究的目的:
- 开发和测试数据驱动的方法,以建立ANN之间的等效类.
- 探索网络输出和内部神经元激活之间的转换.
- 为了证明这种方法在各种学习任务中的适用性.
主要方法:
- 利用与Mahalanobis类似的指标的扩散图来构建ANN之间的数据驱动转换.
- 考虑基于网络输出和中间神经元激活的转换.
- 应用惠特尼定理来确定重建所需的测量.
主要成果:
- 成功建立了数据驱动的转换,表明ANN对之间的等价性.
- 证明了算法的有效性在包括标量函数,向量场和图像表示在内的任务上.
- 展示了从内部激活的部分观测中对网络输入和输出的重建.
结论:
- 开发的方法为识别等效的人工神经网络提供了一个框架.
- 这种等价性构造与转移学习和比较不同的机器学习工具有关.
- 这些发现有助于更深入地了解神经网络的关系和能力.
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