1 citations · 1 across the 5 of their papers we have counts for
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Identification of Bivariate Causal Directionality Based on Anticipated Asymmetric Geometries
Alex Glushkovsky
Identification of causal directionality in bivariate numerical data is a fundamental research problem with important practical implications. This paper presents two alternative met…
Dual Signal Decomposition of Stochastic Time Series
Alex Glushkovsky
The decomposition of a stochastic time series into three component series representing a dual signal - namely, the mean and dispersion - while isolating noise is presented. The dec…
Alternatives of Unsupervised Representations of Variables on the Latent Space
Alex Glushkovsky
The article addresses the application of unsupervised machine learning to represent variables on the 2D latent space by applying a variational autoencoder (beta-VAE). Representatio…
Time Series of Non-Additive Metrics: Identification and Interpretation of Contributing Factors of Variance by Linear Decomposition
Alex Glushkovsky
The research paper addresses linear decomposition of time series of non-additive metrics that allows for the identification and interpretation of contributing factors (input featur…
Designing Complex Experiments by Applying Unsupervised Machine Learning
Alex Glushkovsky
Design of experiments (DOE) is playing an essential role in learning and improving a variety of objects and processes. The article discusses the application of unsupervised machine…
AI Giving Back to Statistics? Discovery of the Coordinate System of Univariate Distributions by Beta Variational Autoencoder
Alex Glushkovsky
Distributions are fundamental statistical elements that play essential theoretical and practical roles. The article discusses experiences of training neural networks to classify un…