论文标题
学习物理现象的热力学
Thermodynamics of learning physical phenomena
论文作者
论文摘要
热力学可以看作是在高认知水平上的物理表达。因此，最近在许多领域中实现了它作为帮助机器学习程序获得准确和可信度的预测的潜在偏见。我们回顾热力学如何在学习过程中提供有用的见解。同时，我们研究了要描述给定现象的规模之类方面的影响，对于此描述的相关变量的选择或可用于学习过程的不同技术。
Thermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate and credible predictions has been recently realized in many fields. We review how thermodynamics provides helpful insights in the learning process. At the same time, we study the influence of aspects such as the scale at which a given phenomenon is to be described, the choice of relevant variables for this description or the different techniques available for the learning process.
