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 ...

Jang et al. [36] introduced a Convolutional Variational Autoencoder (CVAE) that models the variability in ECG patterns through learned latent distributions, facilitating clustering and anomaly ...

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 ...

Notably, these transformers are equipped with sister OLTC units, and the system also records temperature and other pertinent parameters. To detect anomalies in OLTCs and analyze the generated ...

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.

Monitoring is essential for managing and maintaining industrial equipment by identifying abnormal areas in multivariate time series. This work presents CNN-VAEAT, a new technique for identifying ...

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 ...

Here, we propose DivNoising -- a denoising approach based on fully-convolutional variational autoencoders, overcoming this problem by predicting a whole distribution of denoised images. Our method is ...