When the number of pieces is unknown, we prove that, in terms of the number of distinct linear components, the neural complexity of any CPWL function is at most polynomial growth for low-dimensional inputs and factorial growth for the worst-case scenario, which are significantly better than existing results in the literature.
Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems.
To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i. e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets.
The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications.
We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules.