pith:BHPLCXWY
CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks
CAWI samples randomized neural network weights from data-fitted copulas to capture inter-feature dependence and raise accuracy.
arxiv:2605.12580 v1 · 2026-05-12 · cs.LG
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Claims
CAWI consistently delivers significant improvements in predictive performance over conventional random initialization across 83 diverse classification benchmarks and two biomedical datasets using standard shallow and deep RdNN architectures.
That sampling the input-to-hidden weights from a fitted copula will improve the conditioning and predictive performance of the closed-form output-layer solution without introducing new instabilities or requiring changes to the solver.
CAWI replaces standard random initialization of input-to-hidden weights in randomized neural networks with samples drawn from a data-fitted copula that preserves observed feature dependencies, yielding consistent accuracy gains on 83 classification benchmarks.
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| First computed | 2026-05-18T03:10:01.476463Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
09deb15ed8753d3186ea8e5f592bdde4ec7eeb166719205961ba8b1203f2ec8f
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/BHPLCXWYOU6TDBXKRZPVSK654T \
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Canonical record JSON
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