用变分自编码器无监督学习进化树表示，生成快且精度高。
PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
- 基于自回归启发的编码机制，实现高效并行生成树结构。
- 在多个数据集上展现高分辨率树结构表示能力。
- 适合进化生物学家和机器学习研究者用于树形数据建模。
学习进化树结构的有意义表示对于分析演化关系至关重要。传统的基于距离的方法常依赖特定距离度量，易受其影响且分辨率不足。本文提出进化树变分自编码器（PhyloVAE），一种无监督学习框架，用于树拓扑的表示学习与生成建模。借鉴自回归树拓扑生成的高效编码机制，构建深度潜变量生成模型，支持快速并行化拓扑生成。PhyloVAE结合可学习拓扑特征的协同推断模型，实现对进化树样本的高分辨率表示。大量实验表明，PhyloVAE具备强大的表示学习能力，并能快速生成进化树拓扑。
原文摘要 · Abstract (English)
Learning informative representations of phylogenetic tree structures is essential for analyzing evolutionary relationships. Classical distance-based methods have been widely used to project phylogenetic trees into Euclidean space, but they are often sensitive to the choice of distance metric and may lack sufficient resolution. In this paper, we introduce phylogenetic variational autoencoders (PhyloVAEs), an unsupervised learning framework designed for representation learning and generative modeling of tree topologies. Leveraging an efficient encoding mechanism inspired by autoregressive tree topology generation, we develop a deep latent-variable generative model that facilitates fast, parallelized topology generation. PhyloVAE combines this generative model with a collaborative inference model based on learnable topological features, allowing for high-resolution representations of phylogenetic tree samples. Extensive experiments demonstrate PhyloVAE's robust representation learning capabilities and fast generation of phylogenetic tree topologies.
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