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Kymatio: Scattering Transforms in Python

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arxiv 1812.11214 v3 pith:INJNGSX7 submitted 2018-12-28 cs.LG cs.CVcs.SDeess.ASstat.ML

Kymatio: Scattering Transforms in Python

classification cs.LG cs.CVcs.SDeess.ASstat.ML
keywords scatteringkymatiolearningmemorypackagepythonsignaltransform
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks. All transforms may be executed on a GPU (in addition to CPU), offering a considerable speed up over CPU implementations. The package also has a small memory footprint, resulting inefficient memory usage. The source code, documentation, and examples are available undera BSD license at https://www.kymat.io/

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Cited by 2 Pith papers

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  1. Spatial Neighboring Scattering Transform: A Cross-Channel Amplitude Coupling Measure for EEG Connectivity

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    SNST recovers consistent amplitude-envelope and slow-rhythm-gated coupling in motor-imagery EEG that phase-lag indices do not detect under matched FDR control.

  2. Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG

    eess.SP 2026-07 conditional novelty 5.0

    A single sentence stating the discovery directly. ≤ 300 chars.