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A novel process monitoring approach based on variational recurrent autoencoder
Computers and Chemical Engineering, 2019Qinghua He, Feifan Cheng
exaly
A generative design method of airfoil based on conditional variational autoencoder
Engineering Applications of Artificial IntelligenceXu Wang, Hai Chen, Tun Zhao
exaly
Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization [PDF]
Entropy, 2020 Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node ...
Jun Jin Choong +2 more
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The Variational InfoMax AutoEncoder [PDF]
2020 International Joint Conference on Neural Networks (IJCNN), 2020 The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative generator.
Crescimanna, Vincenzo, Graham, Bruce
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Interpretable Variational Graph Autoencoder with Noninformative Prior [PDF]
Future Internet, 2021 Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data.
Lili Sun, Xueyan Liu, Min Zhao, Bo Yang
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Generative autoencoder to prevent overregularization of variational autoencoder
ETRI JournalIn machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low-dimensional posterior dis-tribution ...
YoungMin Ko, SunWoo Ko, YoungSoo Kim
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Importance weighted variational graph autoencoder [PDF]
Complex & Intelligent SystemsVariational Graph Autoencoder (VGAE) is a widely explored model for learning the distribution of graph data. Currently, the approximate posterior distribution in VGAE-based methods is overly restrictive, leading to a significant gap between the ...
Yuhao Tao +3 more
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Variational Autoencoder for the Prediction of Oil Contamination Temporal Evolution in Water Environments [PDF]
SensorsThe water quality monitoring of large water masses using robotic vehicles is a complex task highly developed in recent years. The main approaches utilize adaptative informative path planning of fleets of autonomous surface vehicles and computer learning ...
Alejandro Casado-Pérez +4 more
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Tree Variational Autoencoder for Code [PDF]
IEEE AccessAutoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software.
Vadim Liventsev +3 more
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