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IEEE19d
Learning Physically Meaningful Representations of Energy Systems with Variational Autoencoders
To do this, we use a variational autoencoder to learn the load behavior of a neighborhood with three houses. Since a variational autoencoder learns a latent representation, we analyzed the possibility ...
GitHub24d
variational-autoencoder
This repository contains numerous applications of autoencoder neural networks. Projects include image denoising, detection of infected cells, and processing of the MNIST dataset. Each application ...
Frontiers27d
Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition
Methods: This study integrates rainfall, surface displacement, and vertical displacement monitoring data, and proposes an automatic failure mode identification method based on deep convolutional ...
C&EN27d
Transformation-Free Generation of a Quasi-Diabatic Representation from the State-Average Orbital-Optimized Variational Quantum Eigensolver
IT4Innovations, VSB─Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic ...
IEEE29d
Improved Disentangled Speech Representations Using Contrastive Learning in Factorized Hierarchical Variational Autoencoder
Abstract: Leveraging the fact that speaker identity and content vary on different time scales, factorized hierarchical variational autoencoder (FHVAE) uses different latent variables to symbolize ...