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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…
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Keywords:
reconstruction;
leibler divergence;
variational autoencoders;
reconstruction error ... See more keywords
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Published in 2019 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2019.2951221
Abstract: Upper and lower bounds on the minimum mean square error for additive noise channels are derived when the input distribution is constrained to be close to a Gaussian reference distribution in terms of the Kullback–Leibler…
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Keywords:
input distribution;
noise channels;
leibler divergence;
additive noise ... See more keywords
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Published in 2018 at "Mathematical Problems in Engineering"
DOI: 10.1155/2018/8973131
Abstract: Iterative reconstruction (IR) algorithms based on the principle of optimization are known for producing better reconstructed images in computed tomography. In this paper, we present an IR algorithm based on minimizing a symmetrized Kullback-Leibler divergence…
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Keywords:
reconstruction;
kullback leibler;
symmetrized kullback;
divergence ... See more keywords
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Published in 2023 at "Entropy"
DOI: 10.3390/e25020326
Abstract: Queuing networks (QNs) are essential models in operations research, with applications in cloud computing and healthcare systems. However, few studies have analyzed the cell’s biological signal transduction using QN theory. This study entailed the modeling…
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Keywords:
leibler divergence;
signal transduction;
transduction;
cell signal ... See more keywords