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Mathieu Lefort
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2020 – today
- 2023
- [c25]Frédéric Armetta, Anthony Baccuet, Mathieu Lefort:
L'apprentissage algorithmique, une nouvelle étape pour l'IA. Une application aux opérations arithmétiques. CNIA 2023: 31-39 - [c24]Simon Forest, Jean-Charles Quinton, Mathieu Lefort:
Champ neuronal et apprentissage profond de topologies pour la fusion multimodale. CNIA 2023: 40-49 - [c23]Anaëlle Badier, Mathieu Lefort, Marie Lefèvre:
Recommendation Model for an After-School E-learning Mobile Application. CSEDU (2) 2023: 80-87 - [c22]Alexandre Devillers, Mathieu Lefort:
EquiMod: An Equivariance Module to Improve Visual Instance Discrimination. ICLR 2023 - [c21]Anaëlle Badier, Mathieu Lefort, Marie Lefèvre:
Understanding the Usages and Effects of a Recommendation System in a Non-formal Learning Context. ITS 2023: 54-65 - [i2]Lucien Tisserand, Frédéric Armetta, Heike Baldauf-Quilliatre, Antoine Bouquin, Salima Hassas, Mathieu Lefort:
Sequential annotations for naturally-occurring HRI: first insights. CoRR abs/2308.15097 (2023) - 2022
- [j2]Simon Forest, Jean-Charles Quinton, Mathieu Lefort
:
A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect. Neural Comput. 34(8): 1701-1726 (2022) - [c20]Alexandre Devillers, Valentin Chaffraix, Frédéric Armetta, Stefan Duffner, Mathieu Lefort:
The Impact of Action in Visual Representation Learning. ICDL 2022: 30-35 - [c19]Simon Forest, Jean-Charles Quinton, Mathieu Lefort:
Combining Manifold Learning and Neural Field Dynamics for Multimodal Fusion. IJCNN 2022: 1-8 - [i1]Alexandre Devillers, Mathieu Lefort:
EquiMod: An Equivariance Module to Improve Self-Supervised Learning. CoRR abs/2211.01244 (2022) - 2021
- [c18]Ruiqi Dai, Mathieu Lefort, Frédéric Armetta, Mathieu Guillermin, Stefan Duffner:
Self-supervised Continual Learning for Object Recognition in Image Sequences. ICONIP (5) 2021: 239-247 - [c17]Ruiqi Dai, Mathieu Lefort
Novelty detection for unsupervised continual learning in image sequences. ICTAI 2021: 493-500 - 2020
- [c16]Bastien Nollet, Mathieu Lefort
Learning Arithmetic Operations With A Multistep Deep Learning. IJCNN 2020: 1-8
2010 – 2019
- 2019
- [c15]Laurianne Charrier, Alisa Rieger, Alexandre Galdeano, Amélie Cordier, Mathieu Lefort, Salima Hassas:
The RoPE Scale: a Measure of How Empathic a Robot is Perceived. HRI 2019: 656-657 - 2017
- [j1]Victor Lequay, Mathieu Lefort
Ajustement diffus et adaptatif de la consommation électrique résidentielle par un système multi-agent auto-adaptatif. Rev. d'Intelligence Artif. 31(4): 427-448 (2017) - 2016
- [c14]Alexander Gepperth, Mathieu Lefort:
Learning to be attractive: Probabilistic computation with dynamic attractor networks. ICDL-EPIROB 2016: 270-277 - [c13]Victor Lequay, Mathieu Lefort, Saber Mansour, Salima Hassas:
Ajustement Diffus et Adaptatif de la Consommation Électrique Résidentielle par un Système Multi-Agents. JFSMA 2016: 171-180 - [c12]Victor Lequay, Mathieu Lefort
Flexible Load Shedding Using Gossip Communication in a Multi-agents System. SASO 2016: 31-39 - 2015
- [c11]Alexander Gepperth, Mathieu Lefort, Thomas Hecht:
Resource-efficient Incremental learning in very high dimensions. ESANN 2015 - [c10]Thomas Hecht, Mathieu Lefort, Alexander Gepperth:
Using self-organizing maps for regression: the importance of the output function. ESANN 2015 - [c9]Mathieu Lefort, Alexander Gepperth:
Active learning of local predictable representations with artificial curiosity. ICDL-EPIROB 2015: 228-233 - [c8]Alexander Gepperth, Thomas Hecht, Mathieu Lefort
Biologically inspired incremental learning for high-dimensional spaces. ICDL-EPIROB 2015: 269-275 - [c7]Mathieu Lefort, Alexander Gepperth:
Learning of local predictable representations in partially learnable environments. IJCNN 2015: 1-8 - 2014
- [c6]Mathieu Lefort, Alexander Gepperth:
Discrimination of visual pedestrians data by combining projection and prediction learning. ESANN 2014 - [c5]Alexander Gepperth, Mathieu Lefort
Latency-Based Probabilistic Information Processing in Recurrent Neural Hierarchies. ICANN 2014: 715-722 - [c4]Mathieu Lefort, Thomas Kopinski, Alexander Gepperth:
Multimodal space representation driven by self-evaluation of predictability. ICDL-EPIROB 2014: 319-324 - [c3]Mathieu Lefort, Alexander Gepperth:
PROPRE: PROjection and PREdiction for multimodal correlations learning. An application to pedestrians visual data discrimination. IJCNN 2014: 2718-2725 - 2013
- [c2]Mathieu Lefort, Yann Boniface, Bernard Girau:
SOMMA: Cortically inspired paradigms for multimodal processing. IJCNN 2013: 1-8 - 2012
- [b1]Mathieu Lefort:
Apprentissage spatial de corrélations multimodales par des mécanismes d'inspiration corticale. (Spatial learning of multimodal correlations in a cortically inspired way). Nancy 2 University, France, 2012 - 2011
- [c1]Mathieu Lefort, Yann Boniface, Bernard Girau:
Unlearning in the BCM Learning Rule for Plastic Self-organization in a Multi-modal Architecture. ICANN (1) 2011: 93-100
Coauthor Index
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