NeurIPS 2018poster132 citations
Deepcode: Feedback Codes via Deep Learning
Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Abstract
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide- ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly beats state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so have ever been successfully constructed.
BibTeX
@inproceedings{NEURIPS2018_31f81674,
author = {Kim, Hyeji and Jiang, Yihan and Kannan, Sreeram and Oh, Sewoong and Viswanath, Pramod},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Deepcode: Feedback Codes via Deep Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/31f81674a348511b990af268ca3a8391-Paper.pdf},
volume = {31},
year = {2018}
}