18 citations · 19 across the 5 of their papers we have counts for
9 papers
White Paper Machine Learning in Certified Systems
Hervé Delseny, Christophe Gabreau, Adrien Gauffriau +25
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving veh…
Dataset Definition Standard (DDS)
Cyril Cappi, Camille Chapdelaine, Laurent Gardes +4
This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks. This documen…
Local Propagation for Few-Shot Learning
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard
The challenge in few-shot learning is that available data is not enough to capture the underlying distribution. To mitigate this, two emerging directions are (a) using local image…
Ensuring Dataset Quality for Machine Learning Certification
Sylvaine Picard, Camille Chapdelaine, Cyril Cappi +4
In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing stand…
Few-Shot Few-Shot Learning and the role of Spatial Attention
Yann Lifchitz, Yannis Avrithis, Sylvaine Picard
Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representat…
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error
Michel Moukari, Loïc Simon, Sylvaine Picard +1
As deep learning applications are becoming more and more pervasive in robotics, the question of evaluating the reliability of inferences becomes a central question in the robotics…