Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks. (arXiv:2112.04364v2 [cs.LG] UPDATED)

Motivated by the learned iterative soft thresholding algorithm (LISTA), we
introduce a general class of neural networks suitable for sparse reconstruction
from few linear measurements. By allowing a wide range of degrees of
weight-sharing between the layers, we enable a unified analysis for very
different neural network types, ranging from recurrent ones to networks more
similar to standard feedforward neural networks. Based on training samples, via
empirical risk minimization we aim at learning the optimal network parameters
and thereby the …

algorithms arxiv networks neural networks