Publication

Hyper-Graph-Network Decoders for Block Codes

Neural Information Processing Systems (NeurIPS)


Abstract

Neural decoders were shown to outperform classical message passing techniques for short BCH codes. In this work, we extend these results to much larger families of algebraic block codes, by performing message passing with graph neural networks. The parameters of the sub-network at each variable-node in the Tanner graph are obtained from a hypernetwork that receives the absolute values of the current message as input. To add stability, we employ a simplified version of the arctanh activation that is based on a high order Taylor approximation of this activation function. Our results show that for a large number of algebraic block codes, from diverse families of codes (BCH, LDPC, Polar), the decoding obtained with our method outperforms the vanilla belief propagation method as well as other learning techniques from the literature.

Related Publications

All Publications

Interspeech - October 12, 2021

LiRA: Learning Visual Speech Representations from Audio through Self-supervision

Pingchuan Ma, Rodrigo Mira, Stavros Petridis, Björn W. Schuller, Maja Pantic

ICML - July 18, 2021

Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization

David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Maximilian Balandat

IEEE Transactions on Image Processing Journal - March 9, 2021

Inspirational Adversarial Image Generation

Baptiste Rozière, Morgane Rivière, Olivier Teytaud, Jérémy Rapin, Yann LeCun, Camille Couprie

ICML - July 12, 2020

Lookahead-Bounded Q-Learning

Ibrahim El Shar, Daniel Jiang

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy