CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks
CAWI improves randomized neural networks by modeling feature dependencies.
Randomized neural networks (RdNNs) often ignore feature correlations, degrading performance. CAWI uses copula-based weight initialization to capture inter-feature dependencies, improving conditioning and predictions without backpropagation. Validated on standard benchmarks, it outperforms conventional methods in accuracy and stability.