# Neural Decoding with Optimization of Node Activations

arXiv: https://arxiv.org/abs/2206.00786 · HTML: https://mlsanity.com/paper/2206.00786/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [Deepcode: Feedback Codes via Deep Learning](https://mlsanity.com/paper/1807.00801/) (2018)
- [Hyper-Graph-Network Decoders for Block Codes](https://mlsanity.com/paper/1909.09036/) (2019)
- [Neural Offset Min-Sum Decoding](https://mlsanity.com/paper/1701.05931/) (2017)
- [Fast Deep Learning for Automatic Modulation Classification](https://mlsanity.com/paper/1901.05850/) (2019)
- [Cyclically Equivariant Neural Decoders for Cyclic Codes](https://mlsanity.com/paper/2105.05540/) (2021)
- [Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders](https://mlsanity.com/paper/1905.08795/) (2019)
- [Pruning Neural Belief Propagation Decoders](https://mlsanity.com/paper/2001.07464/) (2020)
- [Active Deep Decoding of Linear Codes](https://mlsanity.com/paper/1906.02778/) (2019)
- [Ensemble Wrapper Subsampling for Deep Modulation Classification](https://mlsanity.com/paper/2005.04586/) (2020)
- [Learning from the Syndrome](https://mlsanity.com/paper/1810.10902/) (2018)

## Built on by (most cited first, 4 of 4)

- [Boosting Learning for LDPC Codes to Improve the Error-Floor Performance](https://mlsanity.com/paper/2310.07194/) (2023)
- [Generalization Bounds for Neural Belief Propagation Decoders](https://mlsanity.com/paper/2305.10540/) (2023)
- [Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for 6G Networks](https://mlsanity.com/paper/2405.13413/) (2024)
- [A Scalable Graph Neural Network Decoder for Short Block Codes](https://mlsanity.com/paper/2211.06962/) (2022)

## Abstract (arXiv)

The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered. It is shown that the neural decoder can be improved with two novel loss terms on the node's activations. The first loss term imposes a sparse constraint on the node's activations. Whereas, the second loss term tried to mimic the node's activations from a teacher decoder which has better performance. The proposed method has the same run time complexity and model size as the neural Belief Propagation decoder, while improving the decoding performance by up to 1.1dB on BCH codes.