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Auteur Zhiyuan Liu |
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Measuring accessibility of bus system based on multi-source traffic data / Yufan Zuo in Geo-spatial Information Science, vol 23 n° 3 (September 2020)
[article]
Titre : Measuring accessibility of bus system based on multi-source traffic data Type de document : Article/Communication Auteurs : Yufan Zuo, Auteur ; Zhiyuan Liu, Auteur ; Xiao Fu, Auteur Année de publication : 2020 Article en page(s) : pp 248 - 257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] accessibilité
[Termes IGN] approche holistique
[Termes IGN] données multisources
[Termes IGN] données spatiotemporelles
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] planification urbaine
[Termes IGN] point d'intérêt
[Termes IGN] Shenzhen
[Termes IGN] trafic routier
[Termes IGN] transport collectifRésumé : (auteur) Accessibility is a representative indicator for evaluating the supply of bus system. Traditional studies have evaluated the accessibility from different aspects. Considering the interaction among land use, bus timetable arrangement and individual factors, a more holistic accessibility measurement is proposed to combine static and dynamic characteristics from multisource traffic data. The rationale of the proposed model is verified by a case study of bus system in Shenzhen, China, which is carried out to find the spatial and temporal discrepancy of service of bus system. It is found that the adjustment of bus schedule to time-varying travel demand can affect accessibility of bus system and that Land-use development, average bus speed and bus facilities all have positive effects on accessibility of bus system. These findings provide significant reference for transport planning and policy-making. The proposed model is not limited to accessibility measuring of bus system, but also applicable to other travel modes. Numéro de notice : A2020-564 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1783189 Date de publication en ligne : 24/07/2020 En ligne : https://doi.org/10.1080/10095020.2020.1783189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95881
in Geo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 248 - 257[article]
Titre : Representation learning for natural language processing Type de document : Monographie Auteurs : Zhiyuan Liu, Éditeur scientifique ; Yankai Lin, Éditeur scientifique ; Maosong Sun, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Importance : 334 p. ISBN/ISSN/EAN : 978-981-1555732-- Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] données massives
[Termes IGN] exploration de données
[Termes IGN] représentation des connaissances
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau sémantique
[Termes IGN] traitement du langage naturelRésumé : (Editeur) This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. Note de contenu :
1. Representation Learning and NLP
1.1 Motivation
1.2 Why Representation Learning Is Important for NLP
1.3 Basic Ideas of Representation Learning
1.4 Development of Representation Learning for NLP
1.5 Learning Approaches to Representation Learning for NLP
1.6 Applications of Representation Learning for NLP
1.7 The Organization of This Book
2. Word Representation
2.1 Introduction
2.2 One-Hot Word Representation
2.3 Distributed Word Representation
2.4 Contextualized Word Representation
2.5 Extensions
2.6 Evaluation
3. Compositional Semantics
3.1 Introduction
3.2 Semantic Space
3.3 Binary Composition
3.4 N-Ary Composition
4. Sentence Representation
4.1 Introduction
4.2 One-Hot Sentence Representation
4.3 Probabilistic Language Model
4.4 Neural Language Model
4.5 Applications
5. Document Representation
5.1 Introduction
5.2 One-Hot Document Representation
5.3 Topic Model
5.4 Distributed Document Representation
5.5 Applications
6. Sememe Knowledge Representation
6.1 Introduction
6.2 Sememe Knowledge Representation
6.3 Applications
7. World Knowledge Representation
7.1 Introduction
7.2 Knowledge Graph Representation
7.3 Multisource Knowledge Graph Representation
7.4 Applications
8. Network Representation
8.1 Introduction
8.2 Network Representation
8.3 Graph Neural Networks
9. Cross-Modal Representation
9.1 Introduction
9.2 Cross-Modal Representation
9.3 Image Captioning
9.4 Visual Relationship Detection
9.5 Visual Question Answering
10. Resources
10.1 Open-Source Frameworks for Deep Learning
10.2 Open Resources for Word Representation
10.3 Open Resources for Knowledge Graph Representation
10.4 Open Resources for Network Representation
10.5 Open Resources for Relation Extraction
11. OutlookNuméro de notice : 26515 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.1007/978-981-15-5573-2 En ligne : http://doi.org/10.1007/978-981-15-5573-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97296