A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional methods ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...

To address this issue, we propose a novel hyperspectral anomaly detection method based on a spatial-spectral joint mask variational autoencoder (VAE). By combining the probabilistic modeling ...