# Learning from the Syndrome

arXiv: https://arxiv.org/abs/1810.10902 · HTML: https://mlsanity.com/paper/1810.10902/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Neural Decoding with Optimization of Node Activations](https://mlsanity.com/paper/2206.00786/) (2022)
- [Adversarial Neural Networks for Error Correcting Codes](https://mlsanity.com/paper/2112.11491/) (2021)
- [A recipe of training neural network-based LDPC decoders](https://mlsanity.com/paper/2205.00481/) (2022)

## Abstract (arXiv)

In this paper, we introduce the syndrome loss, an alternative loss function for neural error-correcting decoders based on a relaxation of the syndrome. The syndrome loss penalizes the decoder for producing outputs that do not correspond to valid codewords. We show that training with the syndrome loss yields decoders with consistently lower frame error rate for a number of short block codes, at little additional cost during training and no additional cost during inference. The proposed method does not depend on knowledge of the transmitted codeword, making it a promising tool for online adaptation to changing channel conditions.