6 papers
How Far Do Simple Transformations Translate Across Text Embedding Models?
Sid Ali Hamideche, Louis Adrien Dufrene, Quentin Lampin +1
We investigate whether simple transformations can translate representations across heterogeneous text embedding models. Understanding how independently trained models organize sema…
Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent
Guillaume Larue, Louis-Adrien Dufrène, Quentin Lampin +2
Parity functions are fundamental Boolean operations with critical applications across machine learning, cryptography, and error correction. Yet, learning high-dimensional parity fu…
Learning Linear Block Codes with Gradient Quantization
Louis-Adrien Dufrène, Quentin Lampin, Guillaume Larue
This study investigates the problem of learning linear block codes optimized for Belief-Propagation decoders significantly improving performance compared to the state-of-the-art. O…
Semantic Communications Services within Generalist Operated Networks
Quentin Lampin, Louis-Adrien Dufrène, Guillaume Larue
This paper addresses the challenge of integrating semantic communication principles into operated networks, traditionally optimized based on network-centric metrics rather than app…
Dynamic Power Allocation in OFDM ISAC for Time of Arrival Estimation
Ali Al Khansa, Giyyarpuram Madhusudan, Guillaume Larue +1
Resource allocation in Integrated Sensing and Communication (ISAC) systems is critical for balancing communication and sensing performance. This paper introduces a novel dynamic po…
Target Handover in Distributed Integrated Sensing and Communication
Yu Ge, Ossi Kaltiokallio, Hui Chen +7
The concept of 6G distributed integrated sensing and communications (DISAC) builds upon the functionality of integrated sensing and communications (ISAC) by integrating distributed…