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 ...

Navigate OpenAI's ChatGPT model lineup with this practical guide to choosing the right version for your specific needs and tasks.

Finally, typical deep learning models in process monitoring are used in a deterministic manner and do not automatically provide confidence levels for each decision. In this paper, a variational ...

The research team used simple signal-to-noise ratio (SNR) observations and different hybrid autoencoder models, including denoising or variational autoencoder combined with recurrent neural network ...

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 ...

In practical chemical production processes, nonlinear dynamic multirate data is widespread and challenging to model. This paper innovatively proposes a temporal–spatial pyramid variational autoencoder ...

To obtain a nuanced understanding of fetal–neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a ...