The field of deep generative modeling has grown rapidly and consistently over the years.
With the availability of massive amounts of training data coupled with advances in scalable …

Biological tree analysis serves as a pivotal tool in uncovering the evolutionary and
differentiation relationships among organisms, genes, and cells. Its applications span …

We propose a novel method, scTree, for single-cell Tree Variational Autoencoders,
extending a hierarchical clustering approach to single-cell RNA sequencing data. scTree …

Industrial robots are vital intelligent equipment in modern industries. Periodic maintenance,
which is costly and cannot prevent unexpected failures, is necessary to reduce the …

Knowledge distillation (KD) enhances student network generalization by transferring dark
knowledge from a complex teacher network. To optimize computational expenditure and …

Due to their unsupervised training and uncertainty estimation, deep Variational
Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series …

This paper introduces Diffuse-TreeVAE, a deep generative model that integrates
hierarchical clustering into the framework of Denoising Diffusion Probabilistic Models …

Finding clusters of data points with similar characteristics and generating new cluster-
specific samples can significantly enhance our understanding of complex data distributions …

Learning informative representations of phylogenetic tree structures is essential for
analyzing evolutionary relationships. Classical distance-based methods have been widely …

The structure of many real-world datasets is intrinsically hierarchical, making the modeling of
such hierarchies a critical objective in both unsupervised and supervised machine learning …