Shapelet-neural-networks for weakly supervised problems

30/06/2018 – 01/07/2018 all-day

Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : IRISA, Rennes
Durée : 36 mois
Contact :
Date limite de publication : 2018-06-30

Contexte :
In the time series analysis domain, very efficient methods have been developed recently for supervised tasks (e.g. classification). Amongst them, shapelet-based models are known to be efficient both in terms of accuracy and complexity [1, 2, 3]. However, in a wide range of applications, very little amount of supervised information is available which prevents from using the above-cited methods directly. Recently, some efforts have been dedicated to design unsupervised methods for time series analysis [4, 5, 6, 7]. These works mainly focus on the particular task of time series clustering.

Sujet :
The aim of this thesis is to explore several weakly supervised tasks for time series analysis. For that purpose, we will be particularly interested in bridging the gap between shapelets and neural networks, in order to learn efficient representations for time series in a weakly supervised context. In [6], we designed LDPS, a model combining shapelet and siamese networks in order to embed time series in a space where Euclidean distance mimics a widely used similarity measure for time series analysis (DTW). We aim at extending this framework to the following tasks :
— time series indexing under DTW : this task is known to be very challenging [8]. We expect that an anytime extension of the LDPS frame-work would be of great help for this task.
— metric learning and semi-supervised learning : we will be interested in extending the LDPS framework for situations where only a few labels are available (semi-supervised task) or where supervised information is available as must-link/cannot-link constraints (as in the metric learning framework)
Other tasks (eg. domain adaptation) will also be considered

Profil du candidat :
Machine Learning and Data mining

Formation et compétences requises :
Master or engineering schools (including a reaserch training)

Adresse d’emploi :
Campus de Beaulieu

Document attaché : Phd_irisa.pdf