Abstract: Machine learning methods offer a shortcut for automated cardiovascular disease diagnosis. However, the high cost of ECG signal annotation, along with insufficient labeled data and class ...

Abstract: We introduce a Conditional Variational Autoencoder (CVAE) surrogate for exoplanet spectral modeling, trained on a wide grid of synthetic spectra spanning key planetary regimes. By tuning ...

CAESAR is a unified framework for spatio-temporal scientific data reduction. The baseline model, CAESAR-V, is built on a variational autoencoder (VAE) with scale hyperpriors and super-resolution ...

Objectives: Clinical trial simulation tools rely on mathematical models for the natural progression of disease, typically fit to historical data. Traditionally, nonlinear mixed effects models have ...

Variational Autoencoders (VAEs) have proven to be powerful generative models in quantitative finance, capable of learning latent representations of market data and generating synthetic scenarios for ...