Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Dupraz, Elsa"'
We explore the problem of distributed Hypothesis Testing (DHT) against independence, focusing specifically on Binary Symmetric Sources (BSS). Our investigation aims to characterize the optimal quantizer among binary linear codes, with the objective o
Externí odkaz:
http://arxiv.org/abs/2410.15839
Publikováno v:
International Zurich Seminar on Information and Communication (IZS), Mar 2024, Zurich, Switzerland
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 focuses on the distributed setup where a source is compressed and tra
Externí odkaz:
http://arxiv.org/abs/2407.06591
This paper investigates practical coding schemes for Distributed Hypothesis Testing (DHT). While the literature has extensively analyzed the information-theoretic performance of DHT and established bounds on Type-II error exponents through quantize a
Externí odkaz:
http://arxiv.org/abs/2405.07697
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 interplay between data reconstruction and learning from the same compressed ob
Externí odkaz:
http://arxiv.org/abs/2404.18688
Autor:
Aliouat, Ahcen, Dupraz, Elsa
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 learning over compressed data, without any prior decoding, holds promise for
Externí odkaz:
http://arxiv.org/abs/2403.10202
This paper investigates Distributed Hypothesis testing (DHT), in which a source $\mathbf{X}$ is encoded given that side information $\mathbf{Y}$ is available at the decoder only. Based on the received coded data, the receiver aims to decide on the tw
Externí odkaz:
http://arxiv.org/abs/2305.06887
Autor:
Kern, Jonathan, Henwood, Sébastien, Mordido, Gonçalo, Dupraz, Elsa, Aïssa-El-Bey, Abdeldjalil, Savaria, Yvon, Leduc-Primeau, François
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 network (DNN) inference accelerators. However, computations in memristor
Externí odkaz:
http://arxiv.org/abs/2205.01707
Autor:
Kern, Jonathan, Dupraz, Elsa, Aïssa-El-Bey, Abdeldjalil, Varshney, Lav R., Leduc-Primeau, François
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 computations, and develop an error propagation model that takes into accou
Externí odkaz:
http://arxiv.org/abs/2109.01520
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 low power supply may introduce faults in the memories used by the decod
Externí odkaz:
http://arxiv.org/abs/2108.11812
Autor:
Dupraz, Elsa, Leduc-Primeau, François
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 evolution (DE) that allows to theoretically study the performance of these LDP
Externí odkaz:
http://arxiv.org/abs/2005.05788