At the industry level, the research suggests that AI-enabled decision support could help firms quantify trade-offs more ...

Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate new ...

KEYWORDS: Variational Auto-Encoder, Speeded-Up Robust Features Hybrd Model JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.4, April 27, 2025 ABSTRACT: Anomaly detection in complex ...

The study introduces a novel hybrid Variational Autoencoder-SURF (VAE-SURF) model for anomaly detection in crowded environments, addressing critical challenges such as scale variance and temporal ...

Specifically, we demonstrate how exploring a variational autoencoder (VAE) latent space, trained on purely normal (valid) data, can effectively fuzz-test representational robustness by anomaly ...

The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation.

Description of the block copolymer SAXS–SEM morphology characterization dataset, image data preprocessing procedures, python packages utilized and the usages of each package, the variational ...

Then, a Variational Graph Autoencoder (VGAE) module for learning heterogeneous networks is established. The heterogeneous network is used as the input of the VGAE module, and then it learns its latent ...