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Titre : Automated machine learning : methods, systems, challenges Type de document : Monographie Auteurs : Frank Hutter, Éditeur scientifique ; Lars Kotthoff, Éditeur scientifique ; Joaquin Vanschoren, Éditeur scientifique Editeur : Springer Nature Année de publication : 2019 Collection : The Springer Series on Challenges in Machine Learning SSCML, ISSN 2520-1328 Importance : 219 p. ISBN/ISSN/EAN : 978-3-030-05318-5 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme d'apprentissage
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] optimisation (mathématiques)Index. décimale : 26.40 Intelligence artificielle Résumé : (Editeur) This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. Note de contenu : AUTOML METHODS
- Hyperparameter Optimization
- Meta-Learning
- Neural Architecture Search
AUTOML SYSTEMS
- Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA
- Hyperopt-Sklearn
- Auto-sklearn: Efficient and Robust Automated Machine Learning
- Towards Automatically-Tuned Deep Neural Networks
- TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning
- The Automatic Statistician
AUTOML CHALLENGES
- Analysis of the AutoML Challenge Series 2015–2018
- Correction to: Neural Architecture SearchNuméro de notice : 26299 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007%2F978-3-030-05318-5 Date de publication en ligne : 04/02/2020 En ligne : https://link.springer.com/book/10.1007%2F978-3-030-05318-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95032 CarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter / Caglar Koylu in Cartography and Geographic Information Science, Vol 46 n° 1 (January 2019)
[article]
Titre : CarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter Type de document : Article/Communication Auteurs : Caglar Koylu, Auteur ; Ryan Larson, Auteur ; Bryce J. Dietrich, Auteur ; Kang-Pyo Lee, Auteur Année de publication : 2019 Article en page(s) : pp 57 - 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse du discours
[Termes IGN] analyse géovisuelle
[Termes IGN] cartogramme
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] corpus
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] exploration de données
[Termes IGN] sentiment
[Termes IGN] Twitter
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Assessing the impact of events on the evolution of online public discourse is challenging due to the lack of data prior to the event and appropriate methodologies for capturing the progression of tenor of public discourse, both in terms of their tone and topic. In this article, we introduce a geovisual analytics framework, CarSenToGram, which integrates topic modeling and sentiment analysis with cartograms to identify the changing dynamics of public discourse on a particular topic across space and time. The main novelty of CarSenToGram is coupling comprehensible spatiotemporal overviews of the overall distribution, topical and sentiment patterns with increasing levels of information supported by zoom and filter, and details-on-demand interactions. To demonstrate the utility of CarSenToGram, in this article, we analyze tweets related to immigration the month before and after the 27 January 2017 travel ban in order to reveal insights into one of the defining moments of President Trump’s first year in office. Not only do we find that the travel ban influenced online public discourse and sentiment on immigration, but it also highlighted important partisan divisions within the US. Numéro de notice : A2019-012 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1510343 Date de publication en ligne : 18/09/2018 En ligne : https://doi.org/10.1080/15230406.2018.1510343 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91661
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Titre : CartAGen: an open source research platform for map generalization Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Imran Lokhat , Auteur ; Cécile Duchêne , Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2019 Autre Editeur : Göttingen : Copernicus publications Collection : Proceedings of the ICA Projets : 1-Pas de projet / Conférence : ICC 2019, 29th International Cartographic Conference ICA, Mapping everything for everyone 15/07/2019 20/07/2019 Tokyo Japon Open Access Proceedings of the ICA Importance : 9 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation
[Termes IGN] CartAGen (plateforme de généralisation)
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] logiciel libre
[Termes IGN] organisme cartographique national
[Termes IGN] plateforme logicielle
[Termes IGN] système multi-agents
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Automatic map generalization is a complex task that is still a research problem and requires the development of research prototypes before being usable in productive map processes. In the meantime, reproducible research principles are becoming a standard. Publishing reproducible research means that researchers share their code and their data so that other researchers might be able to reproduce the published experiments, in order to check them, extend them, or compare them to their own experiments. Open source software is a key tool to share code and software, and CartAGen is the first open source research platform that tackles the overall map generalization problem: not only the building blocks that are generalization algorithms, but also methods to chain them, and spatial analysis tools necessary for data enrichment. This paper presents the CartAGen platform, its architecture and its components. The main component of the platform is the implementation of several multi-agent based models of the literature such as AGENT, CartACom, GAEL, CollaGen, or DIOGEN. The paper also explains and discusses different ways, as a researcher, to use or to contribute to CartAGen. Numéro de notice : C2019-010 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-proc-2-134-2019 Date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.5194/ica-proc-2-134-2019 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93258 Documents numériques
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CartAGen: an open source research platform - pdf éditeurAdobe Acrobat PDF Challenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)
Titre : Challenges in grassland mowing event detection with multimodal Sentinel images Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Sébastien Giordano , Auteur ; Silvia Valero, Auteur ; Clément Mallet , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2019 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : MultiTemp 2019, 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images 05/08/2019 07/08/2019 Shanghai Chine Proceedings IEEE Importance : pp 1 - 4 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] détection d'événement
[Termes IGN] données lidar
[Termes IGN] image multibande
[Termes IGN] image RapidEye
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] image TerraSAR-X
[Termes IGN] méthode robuste
[Termes IGN] nébulosité
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Perceptron multicouche
[Termes IGN] prairie
[Termes IGN] régression
[Termes IGN] réseau neuronal récurrent
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a powerful tool to monitor and investigate Near-Real-Time and seasonally PG cover. Here, pros and cons of Sentinel-2 (S2) and Sentinel-1 (S1) time series exploitation for Mowing Events (MowEve) detection are analysed. A deep-based approach is proposed to obtain consistent and homogeneous biophysical parameter times series for MowEve detection. Recurrent Neural Networks are proposed as regression strategy allowing the synergistic integration of optical and Synthetic Aperture Radar data to reconstruct dense NDVI times series. Experimental results corroborates the interest of deriving consistent and homogeneous series of biophysical parameters for subsequent MowEve detection. Numéro de notice : C2019-028 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/Multi-Temp.2019.8866914 Date de publication en ligne : 29/11/2019 En ligne : https://doi.org/10.1109/Multi-Temp.2019.8866914 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94538 Challenging deep image descriptors for retrieval in heterogeneous iconographic collections / Dimitri Gominski (2019)
Titre : Challenging deep image descriptors for retrieval in heterogeneous iconographic collections Type de document : Article/Communication Auteurs : Dimitri Gominski , Auteur ; Martyna Poreba , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2019 Autre Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Projets : Alegoria / Gouet-Brunet, Valérie Conférence : SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents 21/10/2019 21/10/2019 Nice France Proceedings ACM Importance : pp 31 - 38 Format : 21 x 30 cm Note générale : bibliographie
Preprint publié sur ArXiv https://arxiv.org/abs/1909.08866v1Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse visuelle
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'images
[Termes IGN] collection
[Termes IGN] descripteur
[Termes IGN] données hétérogènes
[Termes IGN] exploration de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] iconographie
[Termes IGN] image multi sources
[Termes IGN] indexation
[Termes IGN] jeu de données
[Termes IGN] recherche d'image basée sur le contenuRésumé : (auteur) This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view contents. For this purpose, we introduce a novel dataset, namely Alegoria dataset, consisting of 12,952 iconographic contents representing landscapes of the French territory, and encapsultating a large range of intra-class variations of appearance which were finely labelled. Six deep features (DELF, NetVLAD, GeM, MAC, RMAC, SPoC) and a hand-crafted local descriptor (ORB) are evaluated against these variations. Their performance are discussed, with the objective of providing the reader with research directions for improving image description techniques dedicated to complex heterogeneous datasets that are now increasingly present in topical applications targeting heritage valorization. Numéro de notice : C2019-022 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : ArXiv Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3347317.3357246 Date de publication en ligne : 19/09/2019 En ligne : https://doi.org/10.1145/3347317.3357246 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93623 Conception et évaluation de techniques d'interaction non-visuelle basées sur un dispositif personnel / Sandra Bardot (2019)PermalinkCorrecting rural building annotations in OpenStreetMap using convolutional neural networks / John E. 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Panorama 2018 / Institut national de l'information géographique et forestière (2012 -) (2019)PermalinkRetour d'expérience de l'école OpenMOLE "ExModelo", organisée en partenariat avec le méso-centre du CRIANN / Mathieu Leclaire (2019)PermalinkPermalinkSeeing the past with computers: Experiments with augmented reality and computer vision for history / Kevin Kee (2019)PermalinkSegmentation d'image par intégration itérative de connaissances / Mahaman Sani Chaibou Salaou (2019)PermalinkSemantic aware quality evaluation of 3D building models : Modeling and simulation / Oussama Ennafii (2019)PermalinkSpatial data management in apache spark: the GeoSpark perspective and beyond / Jia Yu in Geoinformatica, vol 23 n° 1 (January 2019)PermalinkSpatial decision support in urban environments using machine learning, 3D geo-visualization and semantic integration of multi-source data / Nikolaos Sideris (2019)PermalinkStructure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkPermalinkTime-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)PermalinkTowards visual urban scene understanding for autonomous vehicle path tracking using GPS positioning data / Citlalli Gamez Serna (2019)PermalinkPermalinkUrban growth simulations in order to represent the impacts of constructions and environmental constraints on urban sprawl / Mojtaba Eslahi (2019)PermalinkPermalinkDesigning an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes / Sepideh Saeidi in Geocarto international, vol 33 n° 12 (December 2018)PermalinkPoint clouds by SLAM-based mobile mapping systems: accuracy and geometric content validation in multisensor survey and stand-alone acquisition / Giulia Sammartano in Applied geomatics, vol 10 n° 4 (December 2018)Permalink