The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...

Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, China School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China Baseline ...

When two-dimensional electron systems are subjected to magnetic fields at low temperatures, they can exhibit interesting ...

In a study published in Nature Communications, researchers at the University of Wisconsin–Madison introduced a deep learning ...

The model also features a more compressed latent space, which requires less memory while maintaining quality. “With videos, you have a higher compression ratio that allows you, while you’re in ...

Abstract: The Variational Autoencoder (VAE) is a popular generative latent variable model that is often used for representation learning. Standard VAEs assume continuous-valued latent variables and ...

This code aims at quantifying the uncertainty of mooring line fault detection predictions using a multivariate Gaussian mixture variational autoencoder. The scripts included in this code encompass ...

operating entirely within the latent space. Extensive experiments demonstrate that LPO significantly improves the image quality of various diffusion models and consistently outperforms existing DPO ...