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Data-driven method reveals how (un)predictable complex systems can be
A research team has developed a novel method for estimating the predictability of complex dynamical systems. Their work, ...
Scientific Research Publishing2mon
Wang, X., Pi, D., Zhang, X., Liu, H. and Guo, C. (2022) Variational Transformer-Based Anomaly Detection Approach for Multivariate Time Series. Measurement, 191, Article ID: 110791.
ABSTRACT: With the development of the Industrial Internet of Things (IIoT) and cloud computing technologies, intelligent sensors in the field that can generate large volumes of time-series data ...
GitHub2y
Multi-Scale Temporal Variational Autoencoder for Anomaly Detection in Multivariate Time Series
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 ...
IEEE3y
Robust Unsupervised Anomaly Detection With Variational Autoencoder in Multivariate Time Series Data
To meet this challenge, we propose a Multi Scale Convolutional Variational Autoencoder (MSCVAE) to detect anomalies in multivariate time series data. Firstly, multi scale attribute matrices are ...
Frontiers3y
Explore Protein Conformational Space With Variational Autoencoder
Implied time ... in the input and output layers is a fixed number of 4980 while the number of neurons in the encoder and decoder varies with the number of hidden layers. The dimension of the latent ...
Frontiers3y
PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction
Unfortunately, the identification, characterization, and production of AMPs can prove complex and time consuming. Here, we report a peptide generation framework, PepVAE, based around variational ...