While advances in AI have been slow to reach commercial P&C insurance, new trends in data augmentation could help pick up the pace.

VAEs offer another approach to generating synthetic EEG data. Like a conventional autoencoder, a VAE consists of an encoder that transforms raw data into a latent representation and a decoder that ...

In order to accurately predict key variables of complex industrial processes, it is necessary to establish reliable data-driven soft sensing models. The accuracy of soft sensors can be compromised ...

Article citations More>> Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10 ...

scVAG is an innovative framework that integrates Variational Autoencoder (VAE) and Graph Attention Autoencoder (GATE) models for enhanced analysis of single-cell gene expression data. Built upon the ...

Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...

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 ...

Abstract: To deal with the typically insufficiently labeled samples involved in practical spectroscopy measurements, a conditional variational autoencoder (CVAE) is proposed to guide the spectral data ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...