A huge trend in recent earth observation missions is to target high temporal and spatial resolutions (e.g. SENTINEL-2 mission by ESA). Data resulting from these missions can then be used for fine-grained studies in many applications. In this project we will focus on three key environmental issues : agricultural practices and their impact, forest preservation and air quality monitoring. Based on identified key requirements for these application settings, MATS project will feature a complete rethinking of the literature in machine learning for time series, with a focus on large-scale methods that could operate even when little supervised information is available. In more details, MATS will introduce new paradigms in large-scale time series classification, spatio-temporal modeling and weakly supervised approaches for time series. Proposed methods will cover a wide range of machine learning problems including domain adaptation, clustering, metric learning and (semi-)supervised classification, for which dedicated methodology is lacking when time series data is at stake. Methods developed in the project will be made available to the scientific community as well as to practitioners through an open-source toolbox in order to help dissemination to a wide range of application areas. Moreover, the application settings considered in the project will be used to showcase benefits offered by methodologies developed in MATS in terms of time series analysis.
Accueil > Membres > TAVENARD Romain
TAVENARD Romain
- Fonction : Professeur
- Poste : permanent
- Employeur : Université Rennes2
- Publications sur HAL
- Site perso
Équipe de recherche
Recherche
Programmes nationaux sur appels d’offres
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VEGIDAR - Analyse de la Végétation en Ville par couplage LIDAR et PLEIADES | 2014-2017
Le projet vise à exploiter simultanément des données à très haute résolution spatiale optiques et LiDAR afin de proposer de nouvelles méthodologies de détection et de cartographie de la végétation en milieu urbain.
Dernières publications sur HAL
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Watch your Watch: Inferring Personality Traits from Wearable Activity Trackers
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End-to-end learned early classification of time series for in-season crop type mapping
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Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
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Time Series Alignment with Global Invariances
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MAD: Match-And-Deform for Time Series Domain Adaptation
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Scalable clustering of segmented trajectories within a continuous time framework. Application to maritime traffic data
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POT : Python Optimal Transport
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Adversarial Regularization for Explainable-by-Design Time Series Classification
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Fused Gromov-Wasserstein Distance for Structured Objects
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Tslearn, A Machine Learning Toolkit for Time Series Data
