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Published in 2017 at "Scientific Reports"
DOI: 10.1038/s41598-017-11873-y
Abstract: Analyzing large volumes of high-dimensional data is an issue of fundamental importance in data science, molecular simulations and beyond. Several approaches work on the assumption that the important content of a dataset belongs to a…
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Keywords:
dimension;
estimating intrinsic;
datasets minimal;
intrinsic dimension ... See more keywords
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Published in 2017 at "Neural Computation"
DOI: 10.1162/neco_a_00969
Abstract: We propose a method for intrinsic dimension estimation. By fitting the power of distance from an inspection point and the number of samples included inside a ball with a radius equal to the distance, to…
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Keywords:
dimension;
estimation generalized;
dimension estimation;
intrinsic dimension ... See more keywords
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Published in 2021 at "Entropy"
DOI: 10.3390/e23101368
Abstract: Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no…
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Keywords:
package;
dimension;
estimation;
python package ... See more keywords
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Published in 2025 at "Entropy"
DOI: 10.3390/e27040440
Abstract: In this work, we propose to study the collective behavior of different ensembles of neural networks. These sets define and live on complex manifolds that evolve through training. Each manifold is characterized by its intrinsic…
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Keywords:
dimension;
variability;
intrinsic dimension;
network ensembles ... See more keywords