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 ...

PerturbNet is a generative AI model that can predict shifts in cell state—changes in overall gene expression—in response to ...

Variational autoencoders (VAEs) are currently popular deep generative models that demonstrate powerful performance in various applications. However, VAEs have a tendency to vectorize data during ...

This paper presents a novel method for reconstructing EEG signals using a variant of the variational autoencoder (VAE) called beta-VAE. Through extensive evaluation of our model on the DEAP dataset, ...

This paper innovatively proposes a temporal–spatial pyramid variational autoencoder (TS-PVAE) model for the nonlinear temporal–spatial feature pyramid extraction from multirate data. This structure ...

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 ...

In this article, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder (VAE)-based techniques. These techniques require a small number of ...