Generation of whole building renovation scenarios using variational autoencoders
Sign Up to like & getrecommendations! 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
Balancing Reconstruction Error and Kullback-Leibler Divergence in Variational Autoencoders
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Estimating TOA Reliability with Variational Autoencoders
Sign Up to like & getrecommendations! 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
Lifelong Mixture of Variational Autoencoders
Sign Up to like & getrecommendations! 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.
Sign Up to like & getrecommendations! 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
Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice
Sign Up to like & getrecommendations! Published in 2019 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2019.01205
Abstract: International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyze such… read more here.
Keywords: cancer data; autoencoders cancer; variational autoencoders; data integration ... See more keywords
Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks
Sign Up to like & getrecommendations! Published in 2022 at "Biomedicines"
DOI: 10.3390/biomedicines10020223
Abstract: Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed… read more here.
Keywords: brain tumor; classification; brain; generative adversarial ... See more keywords
Optimizing Few-Shot Learning Based on Variational Autoencoders
Sign Up to like & getrecommendations! Published in 2021 at "Entropy"
DOI: 10.3390/e23111390
Abstract: Despite the importance of few-shot learning, the lack of labeled training data in the real world makes it extremely challenging for existing machine learning methods because this limited dataset does not well represent the data… read more here.
Keywords: shot learning; based variational; shot; variational autoencoders ... See more keywords