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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Decouples marginals from dependence in diffusion models to improve tail-risk forecasting in multivariate financial time series.
citing papers explorer
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CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks
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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Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
Decouples marginals from dependence in diffusion models to improve tail-risk forecasting in multivariate financial time series.