In this paper, we explore the effectiveness of three variational methods: density matrix renormalization group (DMRG), Boltzmann machine learning, and variational quantum eigensolver (VQE). We apply ...

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MST-VAE is an unsupervised learning approach for anomaly detection in multivariate time series. Inspired by InterFusion paper, we propose a simple yet effective multi-scale convolution kernels applied ...

Accurate detection of anomalies in multivariate time series data has attracted much attention due to its importance in a wide range of applications. Since it is difficult to obtain accurately labeled ...

The variational autoencoder with 4 hidden layers performed the best with high Spearman and Pearson coefficients and low RMSD. In terms of the encoder ( Figures 4A,B ), a larger number of layers lead ...