Construction of Deep ReLU Nets for Spatially Sparse Learning.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3146062
Abstract: Training an interpretable deep net to embody its theoretical advantages is difficult but extremely important in the community of machine learning. In this article, noticing the importance of spatial sparseness in signal and image processing,… read more here.
Keywords: construction deep; deep relu; deep net; constructive approach ... See more keywords