Methods: We developed a Deep Hierarchical Conditional Variational Autoencoder (CVAE) for de novo ACP design, using transfer learning by initializing the ESM-2 pre-trained encoder. A comprehensive ACP ...

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 ...

Variational Autoencoders (VAE) on MNIST By stuyai, taught and made by Otzar Jaffe This project demonstrates the implementation of a Variational Autoencoder (VAE) using TensorFlow and Keras on the ...

In this article, a conditional variational autoencoder based method is proposed for the probabilistic wind power curve modeling task. To advance the modeling performance, the latent random variable is ...

To summarize, the above results suggest that a variational autoencoder with 4 hidden layers in both of the encoder and decoder modules exhibited the best performance in terms of learning a meaningful ...