标题： 元自编码器：一种发现和表示动态演化类别之间关系的方法 显示英文标题

标题： Meta-autoencoders: An approach to discovery and representation of relationships between dynamically evolving classes

摘要： 自编码器（AE）是一种神经网络，通过自监督训练，为给定类中的所有实例学习一个简洁的参数化表示，以及相应的编码和解码过程。 在这里，我们引入了元自编码器（MAE）的概念：一种用于一组自编码器的自编码器。 给定一组由某些参数值不同而区分开来的类，并且每个类都有一个训练好的AE，该组的MAE是一个神经网络，它已经学习了针对类特定AE的紧凑表示及其相关的编码器和解码器。 这一普遍概念的一个应用是在自然进化的研究和建模中——捕捉多个物种之间动态演化的定义性和区分性特征以及它们与共同祖先之间的关系。 在本中期报告中，我们提供了MAE的构造性定义、初始示例以及机器学习和生物学中的激励研究方向。 显示英文摘要

摘要： An autoencoder (AE) is a neural network that, using self-supervised training, learns a succinct parameterized representation, and a corresponding encoding and decoding process, for all instances in a given class. Here, we introduce the concept of a meta-autoencoder (MAE): an AE for a collection of autoencoders. Given a family of classes that differ from each other by the values of some parameters, and a trained AE for each class, an MAE for the family is a neural net that has learned a compact representation and associated encoder and decoder for the class-specific AEs. One application of this general concept is in research and modeling of natural evolution -- capturing the defining and the distinguishing properties across multiple species that are dynamically evolving from each other and from common ancestors. In this interim report we provide a constructive definition of MAEs, initial examples, and the motivating research directions in machine learning and biology.