论文标题
BOML：python中的一个模块化双层优化库用于元学习
BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning
论文作者
论文摘要
元学习（又名学习学习）最近已成为各种应用程序的有希望的范式。现在有许多元学习方法，每种方法都集中在基础学习者和元学习者的不同建模方面，但是所有这些都可以（重新）作为特定的双层优化问题。这项工作提出了BOML，这是一个模块化的优化库，将几种元学习算法统一到一个共同的双层优化框架中。它提供了层次优化管道以及各种迭代模块，可用于求解元学习方法的主流类别，例如基于元基准的基于元元素和基于元主义的公式。该图书馆用Python编写，可在https://github.com/dut-media-lab/boml上找到。
Meta-learning (a.k.a. learning to learn) has recently emerged as a promising paradigm for a variety of applications. There are now many meta-learning methods, each focusing on different modeling aspects of base and meta learners, but all can be (re)formulated as specific bilevel optimization problems. This work presents BOML, a modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework. It provides a hierarchical optimization pipeline together with a variety of iteration modules, which can be used to solve the mainstream categories of meta-learning methods, such as meta-feature-based and meta-initialization-based formulations. The library is written in Python and is available at https://github.com/dut-media-lab/BOML.
