基于迁移学习的重整化群与自动编码器相似性研究
An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning
摘要：
一段时间以来，物理学家对人工智能（AI）和机器学习（ML）领域产生了浓厚的兴趣，特别倾向于解开学习过程核心的机制。特别是，探索神经网络（NN）的基本数学结构预计不仅有助于我们理解“学习”的认识论意义，而且有可能揭开大脑运作背后的秘密。在这里，值得在物理学核心领域开发的方法与人工智能和机器学习前沿开发的技术之间建立对应关系并进行比较。尽管最近的探索表明了重新规范化组（RG）和深度学习（DL）之间的映射，但我们打算特别研究RG和自动编码器（AE）之间的关系。我们将使用迁移学习（TL）将粗粒度的过程嵌入到神经网络中，并通过一系列测试将其与编码-解码的底层机制进行比较。
英文摘要：
Physicists have had a keen interest in the areas of Artificial Intelligence (AI) and Machine Learning (ML) for some time now, with a special inclination towards unravelling the mechanism at the core of the process of learning. In particular, exploring the underlying mathematical structure of a neural net (NN) is expected to not only help us in understanding the epistemological meaning of `Learning' but also has the potential to unravel the secrets behind the workings of the brain. Here, it is worthwhile to establish correspondences and draw parallels between methods developed in core areas of Physics and the techniques developed at the forefront of AI and ML. Although recent explorations indicating a mapping between the Renormalisation Group(RG) and Deep Learning(DL) have shown valuable insights, we intend to investigate the relationship between RG and Autoencoders(AE) in particular. We will use Transfer Learning(TL) to embed the procedure of coarse-graining in a NN and compare it with the underlying mechanism of encoding-decoding through a series of tests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
