ES-VAE applies TSRVF representation on Kendall's shape manifold inside a VAE to generate and classify skeletal trajectories while removing rigid transformations and timing variability, showing gains over standard VAEs on gait scoring and NTU action recognition.
Nature Methods , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Donor-aware benchmarks show AUROCs up to 0.978 for IBD classification from scRNA-seq using CLR cell-type compositions and GatedStructuralCFN embeddings, with compartment stratification improving both performance and feature stability.
citing papers explorer
-
An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories
ES-VAE applies TSRVF representation on Kendall's shape manifold inside a VAE to generate and classify skeletal trajectories while removing rigid transformations and timing variability, showing gains over standard VAEs on gait scoring and NTU action recognition.
-
Donor-Aware scRNA-seq Benchmarks for IBD Classification
Donor-aware benchmarks show AUROCs up to 0.978 for IBD classification from scRNA-seq using CLR cell-type compositions and GatedStructuralCFN embeddings, with compartment stratification improving both performance and feature stability.