@inproceedings{76f6a8f5ea68459a9503b2f647e4b920,
title = "Neural lattice decoders",
abstract = "Lattice decoders constructed with neural networks are presented. Firstly, we show how the fundamental parallelotope is used as a compact set for the approximation by a neural lattice decoder. Secondly, we introduce the notion of Voronoi-reduced lattice basis. As a consequence, a first optimal neural lattice decoder is built from Boolean equations and the facets of the Voronoi cell. This decoder needs no learning. Finally, we present two neural decoders with learning. It is shown that L1 regularization and a priori information about the lattice structure lead to a simplification of the model.",
keywords = "Closest Vector Problem, Lattice Reduction., Machine Learning, Neural Network",
author = "Vincent Corlay and Boutros, \{Joseph J.\} and Philippe Ciblat and Loic Brunel",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 ; Conference date: 26-11-2018 Through 29-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/GlobalSIP.2018.8646560",
language = "English",
series = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "569--573",
booktitle = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",
}