AEQ-RVAE-ST combines approximate equivariance and progressive sequence lengthening in a recurrent VAE to match or exceed prior generative models on quasi-periodic time series benchmarks.
Nour Neifar, Achraf Ben-Hamadou, Afef Mdhaffar, and Mohamed Jmaiel
2 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
2
Pith papers citing it
6
external citations · external index
verdicts
UNVERDICTED 2representative citing papers
A diffusion model generates synthetic phonocardiogram clips that retain some normal/abnormal discriminative structure (82.8% classifier accuracy) but show reduced envelope periodicity and increased burstiness relative to real clips from the PhysioNet 2016 dataset.
citing papers explorer
-
Approximately Equivariant Recurrent Generative Models for Quasi-Periodic Time Series with a Progressive Training Scheme
AEQ-RVAE-ST combines approximate equivariance and progressive sequence lengthening in a recurrent VAE to match or exceed prior generative models on quasi-periodic time series benchmarks.
-
Diffusion-Based Heart Sound Generation: Evaluation with Physiological Signal Metrics, Classifiers, and Expert Listening
A diffusion model generates synthetic phonocardiogram clips that retain some normal/abnormal discriminative structure (82.8% classifier accuracy) but show reduced envelope periodicity and increased burstiness relative to real clips from the PhysioNet 2016 dataset.