692 citations
- Centre National de la Recherche ScientifiqueFR14 papers
- CentraleSupélecFR6 papers
- Laboratoire des signaux et systèmesFR6 papers
- Université Paris-SudFR4 papers
- École PolytechniqueFR3 papers
- SupélecFR3 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR2 papers
- Centre de Recherche en Économie et StatistiqueFR2 papers
- Centre de Recherche en InformatiqueFR2 papers
- Centre de Recherche en Informatique, Signal et Automatique de LilleFR2 papers
- Centre Inria de SaclayFR2 papers
- DISCO: Systèmes dynamiques interconnectés dans des environnements complexesFR2 papers
41 papers
An Analytical Estimation of Spiking Neural Networks Energy Efficiency
Edgar Lemaire, Loic Cordone, Andrea Castagnetti +3
Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks,…
Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios
Ugo Lecerf, Christelle Yemdji-Tchassi, Sébastien Aubert +1
When learning to behave in a stochastic environment where safety is critical, such as driving a vehicle in traffic, it is natural for human drivers to plan fallback strategies as a…
Instance-aware multi-object self-supervision for monocular depth prediction
Houssem Boulahbal, Adrian Voicila, Andrew Comport
This paper proposes a self-supervised monocular image-to-depth prediction framework that is trained with an end-to-end photometric loss that handles not only 6-DOF camera motion bu…
Lane level context and hidden space characterization for autonomous driving
Corentin Sanchez, Philippe Xu, Alexandre Armand +1
For an autonomous vehicle, situation understand-ing is a key capability towards safe and comfortable decision-making and navigation. Information is in general provided bymultiple s…
Learning from Event Cameras with Sparse Spiking Convolutional Neural Networks
Loïc Cordone, Benoît Miramond, Sonia Ferrante
Convolutional neural networks (CNNs) are now the de facto solution for computer vision problems thanks to their impressive results and ease of learning. These networks are composed…
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Thomas Rojat, Raphaël Puget, David Filliat +3
Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted. This lack of interpretability is a major drawback,…