Travail et subjectivité : perspectives critiques

Autor: Mercure, Daniel, Bourdages-Sylvain, Marie-Pierre

Les auteurs proposent des analyses fines des nouvelles formes de vécus au travail et élaborent des concepts novateurs afin de circonscrire les dimen-sions fondamentales des nouvelles pratiques managériales.

Le modèle cybernétique du contrôle de gestion : vers une anthropologie du contrôle de gestion Pigé Benoit

Autor: Bourdages-Sylvain, Marie-Pierre

Publikováno v: Relations Industrielles / Industrial Relations, 2021 Oct 01. 76(3), 607-610.

Externí odkaz: https://www.jstor.org/stable/27114737

Data mining and data-driven modelling for Air Handling Unit fault detection

Autor: Stéphane Lecoeuche, Tianyun Gao, Sylvain Marié, Patrick Beguery, Simon Thebault, Bartosz Boguslawski

Publikováno v: Clima 2019
Clima 2019, May 2019, Bucharest, Romania
E3S Web of Conferences, Vol 111, p 05009 (2019)

Data-driven automatic fault detection and diagnostics (AFDD) have gained a lot of research attention in recent years. Many existing solutions need to learn from the fault operation data to be able to diagnose the faults. However, these data are usual

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7288113df3e4a30dd349572eafa42139
https://hal.archives-ouvertes.fr/hal-03438801

Fast Bayesian Network Structure Learning using Quasi-Determinism Screening

Autor: Thibaud Rahier, Sylvain Marié, Stéphane Girard, Florence Forbes

Publikováno v: JFRB 2018-9èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes
JFRB 2018-9èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, May 2018, Toulouse, France. pp.14-24
HAL

National audience; Learning the structure of Bayesian networks from data is a NP-Hard problem thatinvolves optimization over a super-exponential sized space. In this work, we show that in mostreal life datasets, a number of the arcs contained in the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::50157bd9f70329d4ebce48c3592708c4
https://hal.science/hal-01691217

A Multi-modal Metric Learning Framework for Time Series kNN Classification

Autor: Michèle Rombaut, Ahlame Douzal-Chouakria, Cao-Tri Do, Sylvain Marié

Publikováno v: Advanced Analysis and Learning on Temporal Data
Advanced Analysis and Learning on Temporal Data, pp.131-143, 2016, 〈10.1007/978-3-319-44412-3_9 〉
Advanced Analysis and Learning on Temporal Data, pp.131-143, 2016, ⟨10.1007/978-3-319-44412-3_9⟩
Lecture Notes in Computer Science ISBN: 9783319444116
AALTD@PKDD/ECML (Revised Selected Papers)

International audience; This work proposes a temporal and frequential metric learning framework for a time series nearest neighbor classification. For that, time series are embedded into a pairwise space where a combination function is learned based

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c186dcb3d2e54b01c848a448157fa7c3
https://hal.archives-ouvertes.fr/hal-01385071