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pith:2026:BHPLCXWYOU6TDBXKRZPVSK654T
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CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks

Mohd. Arshad, M. Tanveer, Mushir Akhtar

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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.

References

105 extracted · 105 resolved · 3 Pith anchors

[1] Langley , title = 2000
[2] T. M. Mitchell. The Need for Biases in Learning Generalizations. 1980 1980
[3] M. J. Kearns , title =
[4] Machine Learning: An Artificial Intelligence Approach, Vol. I. 1983 1983
[5] R. O. Duda and P. E. Hart and D. G. Stork. Pattern Classification. 2000 2000

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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

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09deb15ed8753d3186ea8e5f592bdde4ec7eeb166719205961ba8b1203f2ec8f

Aliases

arxiv: 2605.12580 · arxiv_version: 2605.12580v1 · doi: 10.48550/arxiv.2605.12580 · pith_short_12: BHPLCXWYOU6T · pith_short_16: BHPLCXWYOU6TDBXK · pith_short_8: BHPLCXWY
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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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