论文标题
非线性维度降低的自动编码器问题的元学习公式
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction
论文作者
论文摘要
迅速增长的研究领域是使用机器学习方法（例如自动编码器）来降低科学应用中数据和模型的维度。我们表明，自动编码器的规范配方遭受了几种缺陷，可能会阻碍其性能。使用元学习方法，我们将自动编码器问题重新制定为双层优化程序，该程序明确解决了降低的任务。我们证明，新的配方用规范的自动编码器纠正了已确定的缺陷，提供了一种实用方法来解决它，并以简单的数字说明来展示该配方的强度。
A rapidly growing area of research is the use of machine learning approaches such as autoencoders for dimensionality reduction of data and models in scientific applications. We show that the canonical formulation of autoencoders suffers from several deficiencies that can hinder their performance. Using a meta-learning approach, we reformulate the autoencoder problem as a bi-level optimization procedure that explicitly solves the dimensionality reduction task. We prove that the new formulation corrects the identified deficiencies with canonical autoencoders, provide a practical way to solve it, and showcase the strength of this formulation with a simple numerical illustration.
