Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
Invariant Scattering Convolution Networks
3 Pith papers cite this work, alongside 20 external citations. Polarity classification is still indexing.
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Pith papers citing it
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external citations · Pith
abstract
A wavelet scattering network computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification. It cascades wavelet transform convolutions with non-linear modulus and averaging operators. The first network layer outputs SIFT-type descriptors whereas the next layers provide complementary invariant information which improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State of the art classification results are obtained for handwritten digits and texture discrimination, using a Gaussian kernel SVM and a generative PCA classifier.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A review chapter on tools for inferring galaxy and IGM properties from the 21 cm signal using the initial SKA-Low array configuration.
An overview summarizing SKA-Low 21cm experiments for power spectrum, tomography, 21-cm forest, and cross-correlations, plus critical telescope features, building on the 2015 SKA Science Book.
citing papers explorer
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Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection
Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
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Inferring Cosmology and Astrophysics from the High-redshift 21cm Signal with SKA-Low
A review chapter on tools for inferring galaxy and IGM properties from the 21 cm signal using the initial SKA-Low array configuration.
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Overview of 21cm Experiments at high redshift with SKAO
An overview summarizing SKA-Low 21cm experiments for power spectrum, tomography, 21-cm forest, and cross-correlations, plus critical telescope features, building on the 2015 SKA Science Book.