# Neural Offset Min-Sum Decoding

arXiv: https://arxiv.org/abs/1701.05931 · HTML: https://mlsanity.com/paper/1701.05931/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2017.

## Built on by (most cited first, 24 of 24)

- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes](https://mlsanity.com/paper/2405.01033/) (2024)
- [Autoregressive Belief Propagation for Decoding Block Codes](https://mlsanity.com/paper/2103.11780/) (2021)
- [Deep Quantum Error Correction](https://mlsanity.com/paper/2301.11930/) (2023)
- [FAID Diversity via Neural Networks](https://mlsanity.com/paper/2105.04118/) (2021)
- [CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family](https://mlsanity.com/paper/2210.00313/) (2022)
- [How to Mask in Error Correction Code Transformer: Systematic and Double Masking](https://mlsanity.com/paper/2308.08128/) (2023)
- [Neural Decoding with Optimization of Node Activations](https://mlsanity.com/paper/2206.00786/) (2022)
- [Generalization Bounds for Neural Belief Propagation Decoders](https://mlsanity.com/paper/2305.10540/) (2023)
- [Hybrid Mamba-Transformer Decoder for Error-Correcting Codes](https://mlsanity.com/paper/2505.17834/) (2025)
- [Interpreting Neural Min-Sum Decoders](https://mlsanity.com/paper/2205.10684/) (2022)
- [Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding](https://mlsanity.com/paper/2406.12900/) (2024)
- [Generalization Bounds for Transformer Channel Decoders](https://mlsanity.com/paper/2601.06969/) (2026)
- [Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding](https://mlsanity.com/paper/2102.03959/) (2021)
- [Adversarial Neural Networks for Error Correcting Codes](https://mlsanity.com/paper/2112.11491/) (2021)
- [Friendly Attacks to Improve Channel Coding Reliability](https://mlsanity.com/paper/2401.14184/) (2024)
- [Transformer-Based Decoding in Concatenated Coding Schemes Under Synchronization Errors](https://mlsanity.com/paper/2511.00999/) (2025)
- [Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes](https://mlsanity.com/paper/2512.01389/) (2025)
- [A recipe of training neural network-based LDPC decoders](https://mlsanity.com/paper/2205.00481/) (2022)
- [Neural Network Decoders for Permutation Codes Correcting Different Errors](https://mlsanity.com/paper/2206.03315/) (2022)
- [VT-Former: Efffcient Transformer-based Decoder for Varshamov-Tenengolts Codes](https://mlsanity.com/paper/2502.21060/) (2025)
- [Action-List Reinforcement Learning Syndrome Decoding for Binary Linear Block Codes](https://mlsanity.com/paper/2507.17893/) (2025)
- [Variational Diffusion Channel Decoder](https://mlsanity.com/paper/2605.18902/) (2026)
- [Score Based Error Correcting Code Decoder](https://mlsanity.com/paper/2605.28358/) (2026)

## Abstract (arXiv)

Recently, it was shown that if multiplicative weights are assigned to the edges of a Tanner graph used in belief propagation decoding, it is possible to use deep learning techniques to find values for the weights which improve the error-correction performance of the decoder. Unfortunately, this approach requires many multiplications, which are generally expensive operations. In this paper, we suggest a more hardware-friendly approach in which offset min-sum decoding is augmented with learnable offset parameters. Our method uses no multiplications and has a parameter count less than half that of the multiplicative algorithm. This both speeds up training and provides a feasible path to hardware architectures. After describing our method, we compare the performance of the two neural decoding algorithms and show that our method achieves error-correction performance within 0.1 dB of the multiplicative approach and as much as 1 dB better than traditional belief propagation for the codes under consideration.