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 ...

Neuroscientists have produced the largest wiring diagram and functional map of a mammalian brain to date using tissue from a part of a mouse's cerebral cortex involved in vision, an achievement ...

Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software. We hypothesize that encoder-decoder ...

Introduction: Dementia, characterized by cognitive decline and impaired judgment, imposes a significant economic burden due to its rising prevalence and high diagnostic costs. Recent research has ...

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 ...

Variational autoencoder for protein sequences - add metal binding sites and generate sequences for novel topologies - psipred/protein-vae ...

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 ...

Here we use deep learning techniques, specifically a Rotationally Invariant Variational Autoencoder (rVAE), to analyze fluorescence microscopy images of cyanobacteria bearing a carboxysome reporter ...