From racing circuits to research labs to the entrepreneurial world, Harmoko's path should serve as inspiration for those ...

The study introduces a novel hybrid Variational Autoencoder-SURF (VAE-SURF) model for anomaly detection in crowded environments, addressing critical challenges such as scale variance and temporal ...

Abstract: Time series forecasting based on decomposition method usually decomposes a complex time series into some simple components, such as long-term and seasonal trends, which are more easy to be ...

Time Series Forecasting Based on Structured Decomposition and Variational Autoencoder Abstract: Time series forecasting based on decomposition method usually decomposes a complex time series into some ...

Deep learning methods for generating artificial data in health care include data augmentation by variational autoencoders (VAE) technology. Objective: We aimed to test the feasibility of generating ...

In this article, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder (VAE)-based techniques. These techniques require a small number of ...

generative-model unsupervised-learning multi-label-classification variational-inference network-security anomaly-detection variational-autoencoder lstm-autoencoder time-series-autoencoder Updated on ...

Keywords: feature extraction, variational autoencoder, ECG, electrocardiography, deep learning, explainable AI Citation: Kuznetsov VV, Moskalenko VA, Gribanov DV and Zolotykh NY (2021) Interpretable ...