175 citations · 244 across the 7 of their papers we have counts for
9 papers
LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing
Mélanie Ducoffe, Maxime Carrere, Léo Féliers +4
As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data. Despite the strong practical and c…
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
Thomas Fel, Melanie Ducoffe, David Vigouroux +4
A variety of methods have been proposed to try to explain how deep neural networks make their decisions. Key to those approaches is the need to sample the pixel space efficiently i…
Overestimation learning with guarantees
Adrien Gauffriau, François Malgouyres, Mélanie Ducoffe
We describe a complete method that learns a neural network which is guaranteed to overestimate a reference function on a given domain. The neural network can then be used as a surr…
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
Olga Fink, Qin Wang, Markus Svensén +3
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibr…
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms
Gabriel Rodriguez Garcia, Gabriel Michau, Mélanie Ducoffe +2
The ability to detect anomalies in time series is considered highly valuable in numerous application domains. The sequential nature of time series objects is responsible for an add…
Adversarial Active Learning for Deep Networks: a Margin Based Approach
Melanie Ducoffe, Frederic Precioso
We propose a new active learning strategy designed for deep neural networks. The goal is to minimize the number of data annotation queried from an oracle during training. Previous…
Learning Wasserstein Embeddings
Nicolas Courty, Rémi Flamary, Mélanie Ducoffe
The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions. It has found numer…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
QBDC: Query by dropout committee for training deep supervised architecture
Melanie Ducoffe, Frederic Precioso
While the current trend is to increase the depth of neural networks to increase their performance, the size of their training database has to grow accordingly. We notice an emergen…