Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Integrated multi-omics analysis of ovarian cancer using variational autoencoders

Cloud-VAE: Variational autoencoder with concepts embedded

Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology

Smart anomaly detection for Slocum underwater gliders with a variational autoencoder with long short-term memory networks

Deep generative design of porous organic cages <i>via</i> a variational autoencoder

Interpretable Data Analytics in Blockchain Networks Using Variational Autoencoders and Model-Agnostic Explanation Techniques for Enhanced Anomaly Detection

Exploring descriptors for titanium microstructure via digital fingerprints from variational autoencoders

Exploring descriptors for titanium microstructure via digital fingerprints from variational autoencoders

EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks

EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks

Multi-modal anticipation of stochastic trajectories in a dynamic environment with Conditional Variational Autoencoders

Self-Adaptive Evolutionary Info Variational Autoencoder

Out-of-Distribution Detection with Memory-Augmented Variational Autoencoder

Expanding the chemical space of ionic liquids using conditional variational autoencoders

Self-attention variational autoencoder-based method for incomplete model parameter imputation of digital twin building energy systems

The Identification of Beckwith-Wiedemann Syndrome Through Swap Disentangled Variational Autoencoder

The Identification of Beckwith-Wiedemann Syndrome Through Swap Disentangled Variational Autoencoder

Deciphering galaxy images using machine vision – combining variational autoencoder and principal component analysis for feature extraction

AI models collapse when trained on recursively generated data