4 papers
LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems
Yassine Bougacha, Geoffrey Delhomme, Mélanie Ducoffe +8
This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for ob…
HadamRNN: Binary and Sparse Ternary Orthogonal RNNs
Armand Foucault, Franck Mamalet, François Malgouyres
Binary and sparse ternary weights in neural networks enable faster computations and lighter representations, facilitating their use on edge devices with limited computational power…
How to design a dataset compliant with an ML-based system ODD?
Cyril Cappi, Noémie Cohen, Mélanie Ducoffe +8
This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Le…
Quantized Approximately Orthogonal Recurrent Neural Networks
Armand Foucault, Franck Mamalet, François Malgouyres
In recent years, Orthogonal Recurrent Neural Networks (ORNNs) have gained popularity due to their ability to manage tasks involving long-term dependencies, such as the copy-task, a…