Materials design stands to be one of the most promising applications of quantum computing. However, the presence of noise in near-term quantum devices restricts quantum simulations of materials to ...

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 ...

This study introduces a novel approach to music generation using a Variational Autoencoder (VAE) model, which incorporates style embeddings for enhanced control over the generated music. The model ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Background: Artificial patient technology could transform health care by accelerating diagnosis, treatment, and mapping clinical pathways. Deep learning methods for generating artificial data in ...

This block can seamlessly be integrated into an existing autoencoder architecture to facilitate SU analysis. 2.4 Mathematical foundations of the autoencoder In order to perform the endmember ...

By combining autoencoder (AE) and convolutional neural networks (CNNs), a reference-free approach, SCDA (Sparse Convolutional Denoising Autoencoder), was used for genotype imputation (Chen and Shi, ...