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Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity
[article]
2019
arXiv
pre-print
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Drawing inspiration from information theory and wireless communications, we demonstrate the intersection of coding theory and deep learning through the Grassmannian subspace packing problem in CNNs. ...
We show that Grassmannian packings, especially in the initial layers, address kernel sparsity and encourage diversity, while improving classification accuracy across shallow and deep CNNs with better convergence ...
Acknowledgments We would like to acknowledge Matthew McAteer for helpful discussions and contrbution of designs and plots for Figures 5 and 6 . ...
arXiv:1911.07418v1
fatcat:xufyag6rnfctjhbtgeqdphpdie
A Systematic Review on NOMA Variants for 5G and Beyond
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this technique, spreading sequences are obtained by solving the Grassmannian line packing problem to maximize the minimum chordal distance between spreading codes. ...
In contrast to [257] and [258] , the authors in [259] proposed a low complexity subspace detectors for THz MIMO-NOMA systems. ...
doi:10.1109/access.2021.3081601
fatcat:mzmmr3km5zehhh3fnupyelfo3e