Presents SE-WaveNet with weight-tied dilated convolutions plus wavelet and spectral components that reproduces empirical scaling collapse on financial returns while using L times fewer convolutional parameters.
Multifractality in human heartbeat dynamics
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A fractional dynamical networks ML framework detects cognitive fatigue transitions from EEG with 93.33% accuracy and 95% AUROC by capturing non-Markovian interdependencies via multifractal signatures.
citing papers explorer
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Scale-Equivariant Generative Forecasting: Weight-Tied Dilated Convolutions, Wavelet Scattering Inputs, and Spectral-Consistency Training for Self-Similar Time Series
Presents SE-WaveNet with weight-tied dilated convolutions plus wavelet and spectral components that reproduces empirical scaling collapse on financial returns while using L times fewer convolutional parameters.
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Non-Markovian Dynamical Systems Modeling of Electroencephalogram-based Brain Activity for Anticipating the Cognitive Fatigue Level
A fractional dynamical networks ML framework detects cognitive fatigue transitions from EEG with 93.33% accuracy and 95% AUROC by capturing non-Markovian interdependencies via multifractal signatures.