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 ...

Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature. Within the landscape of modular approaches, a ...

Through a series of numerical experiments, we demonstrate the interpretative power of the variational autoencoder-neural circuit policy model and the utility of the automatic latent perturbation tool ...

Subsequently, we detail the architecture and training process of the proposed autoencoder model, and present the results of generating MRI images for ASD and non-ASD patients. Following this, we ...

This repository contains a Jupyter notebook implementing a Vanilla Variational Autoencoder (VAE) for image generation. The VAE is a powerful generative model that learns to encode images into a latent ...

Variational Autoencoder in tensorflow and pytorch Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more ...

Based on the abovementioned considerations, the work exploits deep VAEs by leveraging the latent space of a deterministic autoencoder (DAE). This approach comprises two stages: training a DAE to ...