Although this can be supported by Variational AutoEncoder (VAE), interaction data in group recommendation are highly sparse and insufficient for VAE model training, resulting in high risks of ...

We propose a novel Variational Autoencoder (VAE) framework for collaborative filtering using contrastive disentanglement. We contrast salient latent features in VAE against the non-salient background.

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

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 ...

X-Sample Contrastive Loss (X-CLR) takes a more refined approach to overcoming these challenges. Traditional contrastive learning methods rely on a rigid binary framework, treating only a single sample ...

We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic ...

A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...

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 ...

This paper is a valuable step in multi-subject behavioral modeling using an extension of the Variational Autoencoder (VAE) framework. Using a novel partition of the latent space and in tandem with a ...