论文标题
分析标签开关和随机梯度下降的动力学模型
Analysis of Kinetic Models for Label Switching and Stochastic Gradient Descent
论文作者
论文摘要
在本文中，我们为标签开关的动力学模型分析提供了一种新颖的方法，该方法用于可以在不同能量景观中随机切换的粒子系统。除了生物学和物理学方面的问题外，我们还证明了随机梯度下降是机器学习中最受欢迎的技术，在这种情况下，可以在考虑及时的变体时可以理解。我们的分析重点是在外部电位集合中的进化情况下，我们为此提供了有关进化以及固定问题的分析和数值结果。
In this paper we provide a novel approach to the analysis of kinetic models for label switching, which are used for particle systems that can randomly switch between gradient flows in different energy landscapes. Besides problems in biology and physics, we also demonstrate that stochastic gradient descent, the most popular technique in machine learning, can be understood in this setting, when considering a time-continuous variant. Our analysis is focusing on the case of evolution in a collection of external potentials, for which we provide analytical and numerical results about the evolution as well as the stationary problem.
