Error-Correcting Codes

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37 papers7 papers in the last four weeks, up 75% on the four weeks before. 0.0% of all new papers.

Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding
May 5, 2026Raphaël Le Bidan, Ahmad Ismail, Elsa Dupraz +1Neural Decoding Error-Correcting Codes
IMT Atlantique, Lab-STICC UMR CNRS 6285, Brest, France
Semantic Error Correction and Decoding for Short Block Codes
Apr 24, 2026Jiafu Hao, Chentao Yue, Wanchun Liu +2Semantic Communication Error-Correcting Codes
School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Learning to Concatenate Quantum Codes
Apr 16, 2026Nico Meyer, Christopher Mutschler, Dominik Seuß +2Quantum Error Correction Error-Correcting Codes
Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Nuremberg, Germany · Machine Learning and Positioning Systems Lab, University of Technology Nuremberg (UTN), Nuremberg, Germany · Center for Artificial Intelligence (CAIRO), Technical University of Applied Sciences Würzburg-Schweinfurt, Germany +1
Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation
Apr 9, 2026Andi Gu, J. Pablo Bonilla Ataides, Mikhail D. Lukin +1Quantum LDPC Codes Quantum Error Correction Decoding
Department of Physics, Harvard University, Cambridge, MA 02138, USA · Harvard Quantum Initiative, Harvard University, Cambridge, MA 02138, USA
Gaussian Shannon: High-Precision Diffusion Model Watermarking Based on Communication
Mar 27, 2026Yi Zhang, Hongbo Huang, Liang-Jie ZhangError-Correcting Codes Diffusion Model Watermarking
Center for AI Services Computing, College of Computer Science and Software Engineering, Shenzhen University
Automated Discovery of Improved Constant Weight Binary Codes
Feb 26, 2026Christopher D. RosinError-Correcting Codes
Repair Brain Damage: Real-Numbered Error Correction Code for Neural Network
Jan 21, 2026Ziqing Lu, Myung Cho, Qiutong Jin +1Neural Network Fault Tolerance Error-Correcting Codes
Dept. of AMCS, University of Iowa, Iowa City, IA, USA · Dept. of ECE, California State University, Northridge, CA, USA · Dept. of EECS, University of California, Berkeley, CA, USA +1

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Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

Raphaël Le Bidan, Ahmad Ismail, Elsa Dupraz, Charbel Abdel Nour

IMT Atlantique, Lab-STICC UMR CNRS 6285, Brest, France

Neural Decoding Error-Correcting Codes

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Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes. However, this approach still has substantial room for improvement. In this paper, we show how to leverage code automorphisms to enhance the ability of existing SBND models to learn and generalize through data augmentation during training and inference. As a result, for the short high-rate codes considered, we obtain models that closely approach MLD performance using small datasets and proper training. Our findings also suggest that many prior results for SBND models in the literature underestimate their true correction capability due to undertraining. Code to reproduce all results is available at: https://github.com/lebidan/sbnd.