CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks

CAWI proposes a new weight initialization framework for Randomized Neural Networks (RdNNs) that addresses the limitation of conventional random initialization ignoring inter-feature dependence. It uses a data-fitted copula to ensure frozen projections respect empirical dependence, improving conditioning and predictive performance.