论文标题
关于隐式正规化：摩尔斯的功能和应用矩阵分解的应用
On implicit regularization: Morse functions and applications to matrix factorization
论文作者
论文摘要
在本文中，我们使用动态系统和摩尔斯函数的不变子空间的概念从头开始重新访问隐式正则化。关键贡献是对隐式正则化的新标准---一个主要的竞争者来解释深层模型（例如神经网络）的概括能力 - 以及研究它的一般蓝图。我们应用这些技术来解决基质分解中隐式正则化的猜想。
In this paper, we revisit implicit regularization from the ground up using notions from dynamical systems and invariant subspaces of Morse functions. The key contributions are a new criterion for implicit regularization---a leading contender to explain the generalization power of deep models such as neural networks---and a general blueprint to study it. We apply these techniques to settle a conjecture on implicit regularization in matrix factorization.
