Tag: signals
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Self-sufficient Independent Component Analysis via KL Minimizing Flows
Self-sufficient Independent Component Analysis via KL Minimizing Flows arXiv:2512.00665v1 Announce Type: new Abstract: We study the problem of learning disentangled signals from data using non-linear Independent Component Analysis (ICA). Motivated by advances in self-supervised learning, we propose to learn self-sufficient signals: A recovered signal should be able to reconstruct a missing value of its own…
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Decoding Nonlinear Signals In Large Observational Datasets
Decoding Nonlinear Signals In Large Observational Datasets Rain, snow, or something In between? The post Decoding Nonlinear Signals In Large Observational Datasets appeared first on Towards Data Science. Fraser King Go to original source
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Matched Topological Subspace Detector
Matched Topological Subspace Detector arXiv:2504.05892v1 Announce Type: new Abstract: Topological spaces, represented by simplicial complexes, capture richer relationships than graphs by modeling interactions not only between nodes but also among higher-order entities, such as edges or triangles. This motivates the representation of information defined in irregular domains as topological signals. By leveraging the spectral dualities…
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Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve
Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve arXiv:2501.08288v1 Announce Type: new Abstract: Across the scientific realm, we find ourselves subtracting or dividing stochastic signals. For instance, consider a stochastic realization, $x$, generated from the addition or multiplication of two stochastic signals $a$ and $b$, namely $x=a+b$ or $x = ab$. For…