Prior-Informed Neural Network Initialization: A Spectral Approach for Function Parameterizing Architectures

ArXi:2603.16376v1 Announce Type: new Neural network architectures designed for function parameterization, such as the Bag-of-Functions (BoF) framework, bridge the gap between the expressivity of deep learning and the interpretability of classical signal processing. However, these models are inherently sensitive to parameter initialization, as traditional data-agnostic schemes fail to capture the structural properties of the target signals, often leading to suboptimal convergence.