用元自编码器捕捉动态演化物种间的共性与差异
Meta-autoencoders: An approach to discovery and representation of relationships between dynamically evolving classes
- 构建多类自编码器的元模型，学习类别间关系的紧凑表示
- 实现对演化中物种特征变化的统一建模与编码
- 适合研究生物演化、动态分类系统的机器学习学者
自编码器（AE）是一种通过自监督训练学习特定类别所有实例紧凑参数化表示及其编码解码过程的神经网络。本文提出元自编码器（MAE）概念：即对一组自编码器进行建模的神经网络。当存在一组由某些参数值差异定义的类别，且每个类别已有训练好的自编码器时，该类别的元自编码器可学习到一类特定自编码器的紧凑表示及其对应的编码与解码过程。该方法在自然演化研究中具有应用潜力——可捕捉多个从共同祖先动态演化而来的物种之间的定义性与区分性特征。本报告提供MAE的构造性定义、初步示例及在机器学习与生物学中的动机研究方向。
原文摘要 · Abstract (English)
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.
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