Search Results for author: Mélanie Ducoffe

Found 9 papers, 3 papers with code

Verification for Object Detection -- IBP IoU

no code implementations30 Jan 2024 Noémie Cohen, Mélanie Ducoffe, Ryma Boumazouza, Christophe Gabreau, Claire Pagetti, Xavier Pucel, Audrey Galametz

We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric.

Handwritten Digit Recognition Object +2

Surrogate Neural Networks Local Stability for Aircraft Predictive Maintenance

no code implementations11 Jan 2024 Mélanie Ducoffe, Guillaume Povéda, Audrey Galametz, Ryma Boumazouza, Marion-Cécile Martin, Julien Baris, Derk Daverschot, Eugene O'Higgins

Surrogate Neural Networks (NN) now routinely serve as substitutes for computationally demanding simulations (e. g., finite element).

Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing

no code implementations8 Jan 2024 Yizhak Elboher, Raya Elsaleh, Omri Isac, Mélanie Ducoffe, Audrey Galametz, Guillaume Povéda, Ryma Boumazouza, Noémie Cohen, Guy Katz

As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot workload and in improving operational safety.

LARD - Landing Approach Runway Detection -- Dataset for Vision Based Landing

1 code implementation5 Apr 2023 Mélanie Ducoffe, Maxime Carrere, Léo Féliers, Adrien Gauffriau, Vincent Mussot, Claire Pagetti, Thierry Sammour

As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data.

Overestimation learning with guarantees

no code implementations26 Jan 2021 Adrien Gauffriau, François Malgouyres, Mélanie Ducoffe

Experiments on real data show that the method makes it possible to use the surrogate function in embedded systems for which an underestimation is critical; when computing the reference function requires too many resources.

Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms

no code implementations14 May 2020 Gabriel Rodriguez Garcia, Gabriel Michau, Mélanie Ducoffe, Jayant Sen Gupta, Olga Fink

Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the time series.

Time Series Time Series Analysis +1

Regret analysis of the Piyavskii-Shubert algorithm for global Lipschitz optimization

no code implementations6 Feb 2020 Clément Bouttier, Tommaso Cesari, Mélanie Ducoffe, Sébastien Gerchinovitz

We consider the problem of maximizing a non-concave Lipschitz multivariate function over a compact domain by sequentially querying its (possibly perturbed) values.

Learning Wasserstein Embeddings

1 code implementation ICLR 2018 Nicolas Courty, Rémi Flamary, Mélanie Ducoffe

Our goal is to alleviate this problem by providing an approximation mechanism that allows to break its inherent complexity.

Dimensionality Reduction Domain Adaptation

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

BIG-bench Machine Learning Clustering +2

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