Learning a Sparse Neural Network using IHT

arXiv:2404.18414v1 Announce Type: new
Abstract: The core of a good model is in its ability to focus only on important information that reflects the basic patterns and consistencies, thus pulling out a clear, noise-free signal from the dataset. This necessitates using a simplified model defined by fewer parameters. The importance of theoretical foundations becomes clear in this context, as this paper relies on established results from the domain of advanced sparse optimization, particularly those addressing nonlinear differentiable functions. The need …

abstract arxiv basic clear core cs.lg dataset focus free good importance information network neural network noise parameters patterns signal simplified type