11 papers
Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding
Raphaël Le Bidan, Ahmad Ismail, Elsa Dupraz +1
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 ha…
Doing More With Less: Towards More Data-Efficient Syndrome-Based Neural Decoders
Ahmad Ismail, Raphaël Le Bidan, Elsa Dupraz +1
While significant research efforts have been directed toward developing more capable neural decoding architectures, comparatively little attention has been paid to the quality of t…
A Novel Theoretical Analysis for Clustering Heteroscedastic Gaussian Data without Knowledge of the Number of Clusters
Dominique Pastor, Elsa Dupraz, Ismail Hbilou +1
This paper addresses the problem of clustering measurement vectors that are heteroscedastic in that they can have different covariance matrices. From the assumption that the measur…
Non-Asymptotic Achievable Rate-Distortion Region for Indirect Wyner-Ziv Source Coding
Jiahui Wei, Philippe Mary, Elsa Dupraz
In the Wyner-Ziv source coding problem, a source has to be encoded while the decoder has access to side information . This paper investigates the indirect setup, in which a…
Learning Variable Node Selection for Improved Multi-Round Belief Propagation Decoding
Ahmad Ismail, Raphaël Le Bidan, Elsa Dupraz +1
Error correction at short blocklengths remains a challenge for low-density parity-check (LDPC) codes, as belief propagation (BP) decoding is suboptimal compared to maximum-likeliho…
Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing
Ismaila Salihou Adamou, Elsa Dupraz, Reza Asvadi +1
This paper addresses the design of practical shortlength coding schemes for Distributed Hypothesis Testing (DHT). While most prior work on DHT has focused on informationtheoretic a…