20 citations · 27 across the 21 of their papers we have counts for
25 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…
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…
Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification
Ahcen Aliouat, Elsa Dupraz
In the emerging field of goal-oriented communications, the focus has shifted from reconstructing data to directly performing specific learning tasks, such as classification, segmen…
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…
Covering Codes as Near-Optimal Quantizers for Distributed Testing Against Independence
Fatemeh Khaledian, Reza Asvadi, Elsa Dupraz +1
We explore the problem of distributed Hypothesis Testing (DHT) against independence, focusing specifically on Binary Symmetric Sources (BSS). Our investigation aims to characterize…
Rate-Loss Regions for Polynomial Regression with Side Information
Jiahui Wei, Philippe Mary, Elsa Dupraz
In the context of goal-oriented communications, this paper addresses the achievable rate versus generalization error region of a learning task applied on compressed data. The study…
Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing
Elsa Dupraz, Ismaila Salihou Adamou, Reza Asvadi +1
This paper investigates practical coding schemes for Distributed Hypothesis Testing (DHT). While the literature has extensively analyzed the information-theoretic performance of DH…
Distributed Source Coding for Parametric and Non-Parametric Regression
Jiahui Wei, Elsa Dupraz, Philippe Mary
The design of communication systems dedicated to machine learning tasks is one key aspect of goal-oriented communications. In this framework, this article investigates the interpla…
Learning on JPEG-LDPC Compressed Images: Classifying with Syndromes
Ahcen Aliouat, Elsa Dupraz
In goal-oriented communications, the objective of the receiver is often to apply a Deep-Learning model, rather than reconstructing the original data. In this context, direct learni…
An Information-Spectrum Approach to Distributed Hypothesis Testing for General Sources
Ismaila Salihou Adamou, Elsa Dupraz, Tad Matsumoto
This paper investigates Distributed Hypothesis testing (DHT), in which a source is encoded given that side information is available at the decoder only. B…
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators
Jonathan Kern, Sébastien Henwood, Gonçalo Mordido +4
Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural ne…
Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering
Jonathan Kern, Elsa Dupraz, Abdeldjalil Aïssa-El-Bey +2
This paper presents a quantized Kalman filter implemented using unreliable memories. We consider that both the quantization and the unreliable memories introduce errors in the comp…
Energy Optimization of Faulty Quantized Min-Sum LDPC Decoders
Mohamed Yaoumi, Jeremy Nadal, Elsa Dupraz +2
The objective of this paper is to minimize the energy consumption of a quantized Min-Sum LDPC decoder, by considering aggressive voltage downscaling of the decoder circuit. Since l…
Noisy Density Evolution With Asymmetric Deviation Models
Elsa Dupraz, François Leduc-Primeau
This paper considers low-density parity-check (LDPC) decoders affected by deviations introduced by the electronic device on which the decoder is implemented. Noisy density evolutio…
Optimized Rate-Adaptive Protograph-Based LDPC Codes for Source Coding with Side Information
Fangping Ye, Elsa Dupraz, Zeina Mheich +1
This paper considers the problem of source coding with side information at the decoder, also called Slepian-Wolf source coding scheme. In practical applications of this coding sche…
Decentralized Clustering on Compressed Data without Prior Knowledge of the Number of Clusters
Elsa Dupraz, Dominique Pastor, François-Xavier Socheleau
In sensor networks, it is not always practical to set up a fusion center. Therefore, there is need for fully decentralized clustering algorithms. Decentralized clustering algorithm…
Rate-Distortion Performance of Sequential Massive Random Access to Gaussian Sources with Memory
Elsa Dupraz, Thomas Maugey, Aline Roumy +1
In Sequential Massive Random Access (SMRA), a set of correlated sources is jointly encoded and stored on a server, and clients want to access to only a subset of the sources. Since…
Decentralized Clustering based on Robust Estimation and Hypothesis Testing
Dominique Pastor, Elsa Dupraz, François-Xavier Socheleau
This paper considers a network of sensors without fusion center that may be difficult to set up in applications involving sensors embedded on autonomous drones or robots. In this c…
K-means Algorithm over Compressed Binary Data
Elsa Dupraz
We consider a network of binary-valued sensors with a fusion center. The fusion center has to perform K-means clustering on the binary data transmitted by the sensors. In order to…
Transmission and Storage Rates for Sequential Massive Random Access
Elsa Dupraz, Thomas Maugey, Aline Roumy +1
This paper introduces a new source coding paradigm called Sequential Massive Random Access (SMRA). In SMRA, a set of correlated sources is encoded once for all and stored on a serv…
Analysis and Design of Finite Alphabet Iterative Decoders Robust to Faulty Hardware
Elsa Dupraz, David Declercq, Bane Vasic +1
This paper addresses the problem of designing LDPC decoders robust to transient errors introduced by a faulty hardware. We assume that the faulty hardware introduces errors during…