Designing Error-Correction Codes by Machine Learning

Error-correction codes are essential to fault-tolerant computational devices. The error-correction model consists of an encoder, an erroneous channel and a decoder. Given a channel and a human-designed encoder, it could be hard to find an efficient decoder, and this problem has been studied with insights from machine learning. Here we move forward and use machine learning to design both the encoder and the decoder. The error-correction capability of the machine-designed encoder and decoder, for a fixed channel, increases during training, which means the machine learns to exploit the redundancy in the transmitted bit string.