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   Articles with "variational autoencoders" as a keyword



Generation of whole building renovation scenarios using variational autoencoders

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recommendations! Published in 2021 at "Energy and Buildings"

DOI: 10.1016/j.enbuild.2020.110520

Abstract: Abstract Buildings consume a huge amount of energy, resulting in a considerable impact on the environment. In Canada, almost 70% of the total energy used by the commercial and institutional sectors was consumed by Heating,… read more here.

Keywords: renovation; variational autoencoders; whole building; using variational ... See more keywords

Mapping Conformational and Kinetic Landscapes of 14-3-3σ/α-Synuclein Assembly via Variational Autoencoders and Markov State Models.

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recommendations! Published in 2025 at "Journal of chemical information and modeling"

DOI: 10.1021/acs.jcim.5c02323

Abstract: The interaction between the molecular chaperone 14-3-3σ and the intrinsically disordered protein α-synuclein is implicated in the pathogenesis of Parkinson's disease, yet its dynamic mechanism remains poorly understood at an atomistic level. The inherent flexibility… read more here.

Keywords: synuclein assembly; mapping conformational; variational autoencoders; state models ... See more keywords

Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full‐Disk Solar Images

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recommendations! Published in 2024 at "Space Weather"

DOI: 10.1029/2023sw003516

Abstract: Deep learning is successful in many fields due to its ability to learn strong feature representations without the need for hand‐crafted features, resulting in models with high representational power. However, many of these models are… read more here.

Keywords: space weather; variational autoencoders; anomaly detection;

Parameter estimation of microlensed gravitational waves with conditional variational autoencoders

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recommendations! Published in 2024 at "Physical Review D"

DOI: 10.1103/physrevd.111.084067

Abstract: Gravitational lensing of gravitational waves (GWs) provides a unique opportunity to study cosmology and astrophysics at multiple scales. Detecting microlensing signatures, in particular, requires efficient parameter estimation methods due to the high computational cost of… read more here.

Keywords: gravitational waves; variational autoencoders; conditional variational; parameter estimation ... See more keywords

Balancing Reconstruction Error and Kullback-Leibler Divergence in Variational Autoencoders

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3034828

Abstract: Likelihood-based generative frameworks are receiving increasing attention in the deep learning community, mostly on account of their strong probabilistic foundation. Among them, Variational Autoencoders (VAEs) are reputed for their fast and tractable sampling and relatively… read more here.

Keywords: reconstruction; leibler divergence; variational autoencoders; reconstruction error ... See more keywords

An Improved Technique for Pneumonia Infected Patients Image Recognition Based on Combination Algorithm of Smooth Generalized Pinball SVM and Variational Autoencoders

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3212535

Abstract: We present a method based on combining a smooth generalized pinball support vector machine (SVM) and variational autoencoders (VAEs) in chest X-ray (CXR) images. We incorporate generalized pinball into the SVM model to address the… read more here.

Keywords: smooth generalized; function; svm variational; generalized pinball ... See more keywords

End-to-End Image Classification and Compression With Variational Autoencoders

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recommendations! Published in 2022 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2022.3182313

Abstract: The past decade has witnessed the rising dominance of deep learning and artificial intelligence in a wide range of applications. In particular, the ocean of wireless smartphones and IoT devices continue to fuel the tremendous… read more here.

Keywords: classification; end; compression; variational autoencoders ... See more keywords
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Estimating TOA Reliability with Variational Autoencoders

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recommendations! Published in 2021 at "IEEE Sensors Journal"

DOI: 10.1109/jsen.2021.3101933

Abstract: Radio frequency (RF)-based localization yields centimeter-accurate positions under mild propagation conditions. However, propagation conditions predominant in indoor environments (e.g. industrial production) are often challenging as signal blockage, diffraction and dense multipath lead to errors in… read more here.

Keywords: reliability variational; toa reliability; localization; variational autoencoders ... See more keywords

Optimizing Satellite Image Analysis: Leveraging Variational Autoencoders Latent Representations for Direct Integration

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recommendations! Published in 2025 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2024.3520879

Abstract: Variational autoencoders (VAEs) have emerged as powerful tools for data compression and representation learning. In this study, we explore the application of VAE-based neural compression models for compressing satellite images and leveraging the latent space… read more here.

Keywords: image; neural compression; latent representations; analysis ... See more keywords
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Lifelong Mixture of Variational Autoencoders

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recommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2021.3096457

Abstract: In this article, we propose an end-to-end lifelong learning mixture of experts. Each expert is implemented by a variational autoencoder (VAE). The experts in the mixture system are jointly trained by maximizing a mixture of… read more here.

Keywords: mixture; mixture variational; variational autoencoders; model ... See more keywords

Learning Hierarchical Variational Autoencoders with Mutual Information Maximization for Autoregressive Sequence Modeling.

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recommendations! Published in 2022 at "IEEE transactions on pattern analysis and machine intelligence"

DOI: 10.1109/tpami.2022.3160509

Abstract: Variational autoencoders (VAEs) are a class of effective deep generative models, with the objective to approximate the true, but unknown data distribution. VAEs make use of latent variables to capture high-level semantics so as to… read more here.

Keywords: mutual information; variational autoencoders; sequence; posterior collapse ... See more keywords