DENOYER Ludovic
تاريـخ المـغادرة : 31/08/2018
2021
2020
CHEN Mickael : Apprendre avec une supervision faible à l'aide de réseaux génératifs profonds .DELASALLES Edouard : Déduire et prédire des représentations dynamiques pour des données temporelles structurées .
2019
LAMPLE Guillaume : Traduction Automatique Non-Supervisée .LÉON Aurélia : Apprentissage séquentiel budgétisé pour la classification extrême et la découverte de hiérarchie en apprentissage par renforcement .
2017
ZIAT Ali : Apprentissage de représentation pour la prédiction et la classification de séries temporelles .CONTARDO Gabriella : Apprentissage Statistique .
2015
MAAG Maria : Apprentissage automatique de fonctions d’anonymisation pour les graphes et les graphes dynamiques .
2014
2013
JACOB Yann : Classification dans les graphes hétérogènes et multi-relationnels: application aux réseaux sociaux .
2012
GAO Sheng : Prédiction de liens par modèles à facteurs latents .
إصدارات 2001-2021
جميع
كتـب
مـنشـورات في مـجلات
فصـول في كـتب
منشورات
Posters
أطروحة
مـنـشـورات أخـرى
2021
P. Erbacher, L. Soulier, L. Denoyer : “State of the Art of User Simulation approaches for conversational information retrieval ”, CSR-Sim4IR 2021 Causality in Search and Recommendation and Simulation of Information Retrieval Evaluation 2021, Online, Canada (2021)T. Véniat, L. Denoyer, M. Ranzato : “Efficient Continual Learning with Modular Networks and Task-Driven Priors ”, 9th International Conference on Learning Representations, ICLR 2021, Vienna, Austria (2021)E. Delasalles, S. Lamprier, L. Denoyer : “Deep dynamic neural networks for temporal language modeling in author communities ”, Knowledge and Information Systems (KAIS), vol. 63 (3), pp. 733-757, (Springer) (2021)
2020
2019
E. Delasalles, S. Lamprier, L. Denoyer : “Dynamic Neural Language Models ”, ICONIP 2019 - 26th International Conference on Neural Information Processing, vol. 11955, Lecture Notes in Computer Science, Sydney, Australia, pp. 282-294 (2019)M. Chen, Th. Artières, L. Denoyer : “Unsupervised Object Segmentation by Redrawing ”, Advances in Neural Information Processing Systems 32 (NIPS 2019), Vancouver, Canada, pp. 12705-12716, (Curran Associates, Inc.) (2019)E. Delasalles, A. Ziat, L. Denoyer, P. Gallinari : “Spatio-temporal neural networks for space-time data modeling and relation discovery ”, Knowledge and Information Systems (KAIS), vol. 61 (3), pp. 1241-1267, (Springer) (2019)E. Delasalles, S. Lamprier, L. Denoyer : “Learning Dynamic Author Representations with Temporal Language Models ”, 2019 IEEE International Conference on Data Mining (ICDM), Beijing, China, pp. 120-129, (IEEE) (2019)W. Aissa, L. Soulier, L. Denoyer : “Modèle de compréhension du besoin en information pour la RI conversationnelle ”, CORIA 2019 - 16e COnférence en Recherche d’Information et Applications, Lyon, France (2019)T. Véniat, O. Schwander, L. Denoyer : “STOCHASTIC ADAPTIVE NEURAL ARCHITECTURE SEARCH FOR KEYWORD SPOTTING ”, ICASSP 2019 - International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom (2019)
2018
W. Aissa, L. Soulier, L. Denoyer : “A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems ”, Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, Bruxelles, Belgium, pp. 33-39 (2018)Th. Artières, Q. Wang, M. Chen, L. Denoyer : “Adversarial learning for modeling human motion ”, The Visual Computer, pp. 1-20, (Springer Verlag) (2018)L. DOS SANTOS, B. Piwowarski, L. Denoyer, P. Gallinari : “Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks ”, ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 12 (5), pp. 1-33, (ACM) (2018)Th. Gerald, N. Baskiotis, L. Denoyer : “Apprentissage stochastique de représentation binaire pour la classification multi-classe dans un grand nombre de catégories ”, Conférence sur l’Apprentissage automatique 2018, Rouen, France (2018)T. Véniat, L. Denoyer : “Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks ”, 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, United States, pp. 3492-3500, (IEEE) (2018)E. Delasalles, S. Lamprier, L. Denoyer : “Apprentissage de l’évolution langagière dans des communautés d’auteurs ”, 15e COnférence en Recherche d'Informations et Applications - CORIA 2018, Rennes, France (2018)M. Chen, L. Denoyer, Th. Artières : “Multi-View Data Generation Without View Supervision ”, 6th International Conference on Learning Representations (ICLR 2018), Vancouver, Canada (2018)Q. Wang, M. Chen, Th. Artières, L. Denoyer : “Transferring Style in Motion Capture Sequences with Adversarial Learning ”, ESANN, Bruges, Belgium (2018)N. Aklil, B. Girard, L. Denoyer, M. Khamassi : “Sequential Action Selection and Active Sensing for Budgeted Localization in Robot Navigation ”, International Journal of Semantic Computing, vol. 12 (01), pp. 109-127, (World Scientific) (2018)G. Contardo, L. Denoyer, Th. Artières : “A Meta-Learning Approach to One-Step Active Learning ”, (2018)M. Chen, L. Denoyer, Th. Artières : “Multi-View Data Generation Without View Supervision ”, (2018)
2017
M. Chen, L. Denoyer : “Multi-view Generative Adversarial Networks ”, ECML PKDD 2017, vol. 10535, ECML PKDD 2017: Machine Learning and Knowledge Discovery in Databases, Skopje, North Macedonia, pp. 175-188 (2017)G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer, M. Ranzato : “Fader Networks: Generating Image Variations by Sliding Attribute Values ”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, United States, pp. 5969-5978 (2017)A. Ziat, E. Delasalles, L. Denoyer, P. Gallinari : “Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery ”, 2017 IEEE International Conference on Data Mining (ICDM), 2017 IEEE International Conference on Data Mining (ICDM), La Nouvelle Orléans, LA, United States, pp. 705-714, (IEEE) (2017)Th. Gerald, N. Baskiotis, L. Denoyer : “Binary Stochastic Representations for Large Multi-class Classification ”, Neural Information Processing, vol. 10634, Lecture Notes in Computer Science, Guangzhou, China, pp. 155-165, (Springer International Publishing), (ISBN: 978-3-319-70086-1) (2017)N. Aklil, B. Girard, M. Khamassi, L. Denoyer : “Sequential Action Selection for Budgeted Localization in Robots ”, IEEE Robotic Computing 2017, Taichung, Taiwan, Province of China, pp. 97-100 (2017)G. Contardo, L. Denoyer, Th. Artières : “A Meta-Learning Approach to One-Step Active-Learning ”, International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms, vol. 1998, CEUR Workshop Proceedings, Skopje, North Macedonia, pp. 28-40, (CEUR) (2017)
2016
Th. Artières, G. Contardo, L. Denoyer : “Recurrent Neural Networks for Adaptive Feature Acquisition ”, 23rd International Conference on Neural Information Processing (ICONIP 2016), vol. 9949, Lecture Notes in Computer Science, Kyoto, Japan, pp. 591-599, (Springer) (2016)A. Ziat, B. Leroy, N. Baskiotis, L. Denoyer : “Joint prediction of road-traffic and parking occupancy over a city with representation learning ”, 19th IEEE International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil (2016)A. Ziat, G. Contardo, N. Baskiotis, L. Denoyer : “Learning Embeddings for Completion and Prediction of Relationnal Multivariate Time-Series ”, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN, Unknown, (2016)A. Léon, L. Denoyer : “Policy-gradient methods for Decision Trees ”, ESANN - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Unknown, (2016)G. Contardo, L. Denoyer, Th. Artières : “Sequential Cost-Sensitive Feature Acquisition ”, IDA 2016, Unknown, (2016)
2015
Th. Gisselbrecht, P. Gallinari, S. Lamprier, L. Denoyer : “WhichStreams: A Dynamic Approach for Focused Data Capture from Large Social Media ”, Ninth International Conference on Web and Social Media, ICWSM 2015, Oxford, United Kingdom, pp. 130-139 (2015)Th. Gisselbrecht, L. Denoyer, P. Gallinari, S. Lamprier : “Apprentissage en temps réel pour la collecte d’information dans les réseaux sociaux. ”, CORIA 2015 - Conférence en Recherche d'Infomations et Applications, Paris, France, pp. 7-22 (2015)A. Ziat, G. Contardo, N. Baskiotis, L. Denoyer : “Car-traffic forecasting: A representation learning approach ”, Workshop MUD, Mining Urban Data 201, Unknown, (2015)A. Léon, L. Denoyer : “Reinforced Decision Trees ”, European Workshop on Reinforcement Learning - EWRL, Unknown, (2015)Th. Gisselbrecht, L. Denoyer, P. Gallinari, S. Lamprier : “Apprentissage en temps réel pour la collecte d’information dans les réseaux sociaux ”, Document numérique - Revue des sciences et technologies de l'information. Série Document numérique, vol. 18 (2-3), pp. 39-58, (Hermès) (2015)
2014
L. Denoyer, P. Gallinari : “Deep Sequential Neural Network ”, Deep Learning and Representation Learning Workshop, NIPS 2014, Montreal, Canada (2014)M. Maag, L. Denoyer, P. Gallinari : “Graph Anonymization using Machine Learning ”, 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, Victoria, Canada, pp. 1111-1118 (2014)G. Contardo, L. Denoyer, Th. Artières, P. Gallinari : “Learning States Representations in POMDP ”, International Conference on Learning Representations, ICLR 2014, Banff, Canada (2014)G. Dulac‑Arnold, L. Denoyer, N. Thome, M. Cord, P. Gallinari : “Sequentially Generated Instance-Dependent Image Representations for Classification ”, International Conference on Learning Representations, ICLR 2014, Banff, Canada (2014)S. Bourigault, C. Lagnier, S. Lamprier, L. Denoyer, P. Gallinari : “Apprentissage de représentation pour la diffusion d’Information dans les réseaux sociaux ”, COnférence en Recherche d'Information et Applications 2014, CORIA 2014, Nancy, France, pp. 155-170 (2014)Y. Jacob, L. Denoyer, P. Gallinari : “Learning latent representations of nodes for classifying in heterogeneous social networks ”, The 7th ACM international conference on Web search and data mining, New York City, United States, pp. 373-382 (2014)S. Bourigault, C. Lagnier, S. Lamprier, L. Denoyer, P. Gallinari : “Learning social network embeddings for predicting information diffusion ”, Proceedings of the 7th ACM international conference on Web search and data mining, New York, United States, pp. 393-402, (ACM) (2014)G. Contardo, L. Denoyer, Th. Artières : “Representation Learning for cold-start recommendation ”, ICLR, Banff, Canada (2014)C. Lagnier, S. Bourigault, S. Lamprier, L. Denoyer, P. Gallinari : “Learning Information Spread in Content Networks ”, ICLR 2014 - International Conference on Learning Representations, CoRR, Banff, Canada, pp. abs/1312.6169 (2014)L. Denoyer, P. Gallinari : “Deep Sequential Neural Networks ”, EWRL 2015 - Workshop Deep Learning NIPS 2014, Unknown, (2014)M. Sebban, L. Denoyer, A. Habrard : “Proceddings of 16e Conférence d’Apprentissage CAp’2014 ”, (2014)
2013
R. Lage, L. Denoyer, P. Gallinari, P. Dolog : “Choosing which message to publish on social networks: A Contextual bandit approach ”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Niagara Falls, Ontario, Canada, pp. 620-627, (IEEE) (2013)Y. Jacob, L. Denoyer, P. Gallinari : “Classification dans les graphes hétérogénes basée sur une représentation latente des noeuds ”, CORIA 2013, Neuchâtel, Switzerland, pp. 85-100 (2013)Sh. Gao, L. Denoyer, P. Gallinari, J. Guo : “Latent factor blockmodel for modelling relational data ”, 35th European Conference on IR Research, ECIR 2013, vol. 7814, Lecture Notes in Computer Science, Moscou, Russian Federation, pp. 447-458, (Springer) (2013)C. Lagnier, L. Denoyer, E. Gaussier, P. Gallinari : “Predicting Information Diffusion in Social Networks using Content and User’s Profiles ”, 35th European Conference on IR Research, ECIR 2013, vol. 7814, Lecture Notes in Computer Science, Moscou, Russian Federation, pp. 74-85 (2013)
2012
L. Denoyer : “Learning with Relational Data: Sequential Models and Propagation Models for Structured Classification and Labeling ”, رخصة بإدارة البحوث, مناقـشـة مـذكـرة 07/12/2012 (2012)G. Dulac‑Arnold, L. Denoyer, Ph. Preux, P. Gallinari : “Sequential approaches for learning datum-wise sparse representations ”, Machine Learning, vol. 89 (1-2), pp. 87-122, (Springer Verlag) (2012)Sh. Gao, L. Denoyer, P. Gallinari, J. Guo : “Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks ”, The Journal of China Universities of Posts and Telecommunications, vol. 19 (2), pp. 172-181 (2012)Sh. Gao, L. Denoyer, P. Gallinari : “Link Prediction via Latent Factor Blockmodel ”, WWW'12 - The 21st International Conference on World Wide Web, Lyon, France, pp. 507-508, (ACM) (2012)Sh. Gao, L. Denoyer, P. Gallinari : “Modeling Relational Data via Latent Factor Blockmodel ”, (2012)G. Dulac‑Arnold, L. Denoyer, P. Gallinari : “Lecture Séquentielle de Documents pour la Classification ”, CORIA, Bordeaux, France, pp. 245-259 (2012)S. Peters, Y. Jacob, L. Denoyer, P. Gallinari : “Iterative Multi-Label Multi-Relational Classification Algorithm for Complex Social Networks ”, Social Network Analysis and Mining, vol. 2 (1), pp. 17-29, (Springer) (2012)G. Dulac‑Arnold, L. Denoyer, Ph. Preux, P. Gallinari : “Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization ”, Machine Learning and Knowledge Discovery in Databases, vol. 7524, Lecture Notes in Computer Science, Bristol, United Kingdom, pp. 180-194, (Springer) (2012)G. Dulac‑Arnold, L. Denoyer, Ph. Preux, P. Gallinari : “Classification Localement Parcimonieuse par Méthodes Séquentielles ”, CAP 2012 - Conférence Francophone sur l'Apprentissage Automatique, Nancy, France (2012)G. Dulac‑Arnold, L. Denoyer, Ph. Preux, P. Gallinari : “Apprentissage par renforcement rapide pour des grands ensembles d’actions en utilisant des codes correcteurs d’erreur ”, Journées Francophones sur la planification, la décision et l'apprentissage pour le contrôle des systèmes - JFPDA 2012, Villers-lès-Nancy, France, pp. 12 p (2012)A. Najar, L. Denoyer, P. Gallinari : “Predicting information diffusion on social networks with partial knowledge ”, WWW'12 - The 21st International Conference on World Wide Web, Lyon, France, pp. 1197-1204, (ACM) (2012)Y. Jacob, L. Denoyer, P. Gallinari : “Apprentissage automatique de la propagation des étiquettes dans les réseaux sociaux multirelationnels ”, Document numérique - Revue des sciences et technologies de l'information. Série Document numérique, vol. 15 (1), pp. 79-99, (Hermès) (2012)F. Maes, L. Denoyer, P. Gallinari : “Corpus-Based Structure Mapping of XML Document Corpora: A Reinforcement Learning Based Model ”, chapter in Modeling, Learning, and Processing of Text Technological Data Structures, vol. 370, Studies in Computational Intelligence, pp. 249-266, (Springer Berlin/Heidelberg), (ISBN: 978-3-642-22612-0) (2012)
2011
Sh. Gao, L. Denoyer, P. Gallinari : “Prédiction de Liens Temporels en Intégrant les Informations de Contenu et de Structure ”, Seconde conférence sur les Modèles et l′Analyse des Réseaux : Approches Mathématiques et Informatique, Grenoble, France, pp. 1-14 (2011)Y. Jacob, L. Denoyer, P. Gallinari : “Classification and Annotation in Social Corpora using Multiple Relations ”, the 20th ACM international conference on Information and knowledge management , CIKM'11, Glasgow, United Kingdom, pp. 1215-1220, (ACM) (2011)Sh. Gao, L. Denoyer, P. Gallinari : “Temporal Link Prediction by Integrating Content and Structure Information ”, the 20th ACM international conference on Information and knowledge management, CIKM'11, Glasgow, United Kingdom, pp. 1169-1174, (ACM) (2011)G. Dulac‑Arnold, L. Denoyer, Ph. Preux, P. Gallinari : “Datum-wise classification. A sequential Approach to sparsity ”, ECML PKDD 2011 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, vol. 6911, Lecture Notes in Computer Science, Athens, Greece, pp. 375-390, (Springer) (2011)G. Dulac‑Arnold, L. Denoyer, P. Gallinari : “Text Classification: A Sequential Reading Approach ”, 33rd European Conference on Information Retrieval (ECIR 2011), vol. 6611, Lecture Notes in Computer Science, Dublin, Ireland, pp. 411-423, (Springer Berlin / Heidelberg) (2011)Y. Jacob, L. Denoyer, P. Gallinari : “Apprentissage des schemas de propagation dans les multi-graphes ”, COnférence en Recherche d'Infomations et Applications - CORIA 2011, Avignon, France, pp. 159-174 (2011)Sh. Gao, L. Denoyer, P. Gallinari : “Link pattern prediction with tensor decomposition in multi-relational networks ”, CIDM 2011 - IEEE Symposium on Computational Intelligence and Data Mining, Paris, France, pp. 333-340, (IEEE) (2011)
2010
A. Sokolov, T. Urvoy, L. Denoyer, O. Ricard : “MADSPAM Consortium at the ECML/PKDD Discovery Challenge 2010 : 1st place for the English quality task ”, ECML/PKDD Discovery Challenge, Barcelone, Spain (2010)Th. Beckers, P. Bellot, G. Demartini, L. Denoyer, Christopher M. De Vries, A. Doucet, Kh. Fachry, N. Fuhr, P. Gallinari, Sh. Geva, W.‑Ch. Huang, T. Iofciu, J. Kamps, G. Kazai, M. Koolen, S. Kutty, M. Landoni, M. Lehtonen, V. Moriceau, R. Nayak, R. Nordlie, N. Pharo, E. Sanjuan, R. Schenkel, X. Tannier, M. Theobald, James A. Thom, A. Trotman, Arjen P. De Vries : “Report on INEX 2009 ”, Sigir Forum, vol. 44 (1), pp. 38-57, (Association for Computing Machinery (ACM)) (2010)L. Denoyer, P. Gallinari : “A Ranking based Model for Automatic Annotation in a Social Network ”, Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, United States, pp. 231-234, (AAAI) (2010)Sh. Gao, L. Denoyer, P. Gallinari : “Prédiction de Motifs Relationnels par Décomposition Tensorielle dans les Réseaux Sociaux ”, Workshop Reiso 2010, Marseille, France, pp. 51-58 (2010)L. Denoyer, P. Gallinari : “Modèles d’Ordonnancement pour l’Annotation Automatique d’Images dans les Réseaux Sociaux ”, CORIA 2010, Sousse, Tunisia (2010)S. Peters, L. Denoyer, P. Gallinari : “Iterative annotation of multi-relational social networks ”, Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on, Unknown, , pp. 96-103 (2010)
2009
R. Nayak, Christopher M. De Vries, S. Kutty, Sh. Geva, L. Denoyer, P. Gallinari : “Overview of the INEX 2009 XML Mining Track: Clustering and Classification of XML Documents ”, 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009, Brisbane, Australia, pp. 366-378, (Springer) (2009)F. Maes, L. Denoyer, P. Gallinari : “Structured Prediction with Reinforcement Learning ”, Machine Learning, vol. 77 (2-3), pp. 271-301, (Springer Verlag) (2009)F. Maes, S. Peters, L. Denoyer, P. Gallinari : “SICA : Simulated Iterative Classification - A new larning method for graph labelling ”, ECML PKDD 2009, vol. 5782, Lecture Notes in Computer Science, Bled, Slovenia, pp. 47-62, (Springer) (2009)A. Fakeri‑Tabrizi, S. Tollari, L. Denoyer, P. Gallinari : “UPMC/LIP6 at ImageCLEFannotation 2009: Large Scale Visual Concept Detection and Annotation ”, CLEF working notes 2009, Corfu, Greece (2009)G. Demartin, L. Denoyer, A. Douce, Kh. Fachry, P. Gallinari, Sh. Gev, W.‑Ch. Huang, T. Iofciu, J. Kamps, G. Kazai, M. Koolen, M. Landoni, R. Nordlie, N. Pharo, R. Schenkel, M. Theobald, A. Trotman, Arjen P. De Vries, A. Woodley, J. Zhu : “Report on INEX 2008 ”, Sigir Forum, vol. 43 (1), pp. 17-36, (Association for Computing Machinery (ACM)) (2009)A. Denoyer, L. Denoyer, J. Halfon, S. Majzoub, P.‑J. Pisella : “A Comparative Study of Aspheric IOLs with Either Negative or No Spherical Aberrations ”, Journal of Cataract and Refractive Surgery, vol. 35 (3), pp. 496-503, (Elsevier) (2009)L. Denoyer, P. Gallinari : “Report on the xml mining classification track at inex 2009 ”, INitiative for the Evaluation of XML Retrieval 2009 Workshop Preproceedings (INEX 2009), Unknown, , pp. 339-343 (2009)
2008
F. Maes, L. Denoyer, P. Gallinari : “Applications of Reinforcement Learning to Structured Prediction ”, European Workshop on Reinforcement Learning, vol. 5323, Lecture Notes in Computer Science, Villeneuve d’Ascq, France, pp. 205-219, (Springer) (2008)L. Denoyer, P. Gallinari : “Report on the XML mining track at INEX 2007 categorization and clustering of XML documents ”, Sigir Forum, vol. 42 (1), pp. 22-28, (Association for Computing Machinery (ACM)) (2008)C. Castillo, K. Chellapilla, L. Denoyer : “Web spam challenge 2008 ”, 4th International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), Beijing, China (2008)F. Maes, L. Denoyer, P. Gallinari : “Apprentissage de conversion de documents semi-structures a partir d’exemples ”, CORIA 2008 - Conférence en Recherche d'Informations et Applications, Tregastel, France, pp. 181-196, (ARIA) (2008)A. Denoyer, L. Denoyer, J. Halfon, P.‑J. Pisella : “Influence of the Corneal Incision Size on Corneal and Ocular Higher-Order Aberrations After Cataract Surgery: Microincision versus Small-Incision ”, Investigative Ophthalmology & Visual Science, vol. 49 (13), pp. 5662-5662, (Association for Research in Vision and Ophthalmology) (2008)A. Denoyer, L. Denoyer, D. Marotte, M. Georget, P.‑J. Pisella : “Intraindividual comparative study of corneal and ocular wavefront aberrations after biaxial microincision versus coaxial small-incision cataract surgery ”, British Journal of Ophthalmology, vol. 92 (12), pp. 1679-1684, (BMJ Publishing Group) (2008)L. Denoyer, P. Gallinari : “Machine Learning for Semi-Structured Multimedia Documents : Application to pornographic filtering and thematic categorization ”, chapter in Machine Learning Techniques for Multimedia Content, Cognitive Technologies, pp. 227-247, (Springer), (ISBN: 978-3-540-75170-0) (2008)
2007
F. Maes, L. Denoyer, P. Gallinari : “Sequence Labelling with Reinforcement Learning and Ranking Algorithms ”, 18th European Conference on Machine Learning, ECML 2007, vol. 4701, Lecture Notes in Computer Science, Warsaw, Poland, pp. 648-657, (Springer) (2007)G. Wisniewski, F. Maes, L. Denoyer, P. Gallinari : “Probabilistic Model for Structured Document Mapping ”, 5th International Conference on Machine Learning and Data Mining for Pattern Recognition (MLDM'07'), vol. 4571, Lecture Notes in Computer Science, Leizig, Germany, pp. 854-867, (Springer) (2007)A. Meyers, N. Ide, L. Denoyer, Y. Shinyama : “Shared Corpora Working Group Report ”, The LAW, Linguistic Annotation Workshop, Prague, Czechia, pp. 184-190, (Association for Computational Linguistics) (2007)C. Castillo, B. Davison, L. Denoyer, P. Gallinari : “Web spam challenge track ii ”, ECML/PKDD Graph Labelling Workshop, Warsaw, Poland (2007)L. Denoyer, P. Gallinari, A.‑M. Vercoustre : “Report on the XML Mining Track at INEX 2005 and INEX 2006, Categorization and Clustering of XML Documents ”, 5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2006, vol. 4518, Lecture Notes in Computer Science, Dagstuhl, Germany, pp. 432-443, (Springer) (2007)L. Denoyer, P. Gallinari : “The Wikipedia XML Corpus ”, Advances in XML Information Retrieval and Evaluation: Fifth Workshop of the INitiative for the Evaluation of XML Retrieval (INEX'06), vol. 4518, Lecture Notes in Computer Science, Dagstuhl, Germany, pp. 12-19 (2007)L. Candillier, L. Denoyer, P. Gallinari, M.‑Ch. Rousset, A. Termier, A.‑M. Vercoustre : “Mining XML Documents ”, chapitre de Data Mining Patterns: New Methods and Applications, pp. 198-219, (Information Science Reference) (2007)G. Wisniewski, F. Maes, L. Denoyer, P. Gallinari : “Modèle probabiliste pour l’extraction de structures dans les documents Web ”, Document numérique - Revue des sciences et technologies de l'information. Série Document numérique, vol. 10 (1), pp. 89-107, (Hermès) (2007)C. Castillo, B. Davison, L. Denoyer, P. Gallinari : “PROCEEDINGS OF THE GRAPH LABELLING WORKSHOP AND WEB SPAM CHALLENGE ”, (2007)
2006
F. Maes, L. Denoyer, P. Gallinari : “XML Structure Mapping, Application to the PASCAL/INEX 2006 XML Document Mining Track ”, Advances in XML Information Retrieval and Evaluation: Fifth Workshop of the INitiative for the Evaluation of XML Retrieval (INEX'06), vol. 4518, Lecture Notes in Computer Science, Dagstuhl, Germany, pp. 540-551, (Springer) (2006)L. Denoyer, P. Gallinari : “The XML Wikipedia Corpus ”, Sigir Forum, vol. 40 (1), pp. 64-69, (Association for Computing Machinery (ACM)) (2006)G. Wisniewski, L. Denoyer, F. Maes, P. Gallinari : “Modèle probabiliste pour l’extraction de structures dans les documents semi-structurés: Application aux documents Web ”, 3e me Conference en Recherche d'Information et Applications (CORIA'06), Lyon, France, pp. 169-180 (2006)
2005
P. Gallinari, G. Wisniewski, F. Maes, L. Denoyer : “Stochastic models for document restructuration ”, ECML'05 Workshop on Relationnal Machine Learning, Porto, Portugal (2005)L. Denoyer, G. Wisniewski, P. Gallinari : “Classification automatique de structures arborescentes à l’aide du noyau de Fisher: Application aux documents XML ”, 6e Congrès européen de science des systèmes, vol. 5, Res-Systemica, Paris, France, (AFSCET) (2005)G. Wisniewski, L. Denoyer, P. Gallinari : “Classification automatique de documents structurés. Application au corpus d’arbres étiquetés de type XML ”, CORIA 2005 - 2e Conférence en Recherche d'Informations et Applications, Grenoble, France, pp. 167-184 (2005)G. Wisniewski, L. Denoyer, P. Gallinari : “Restructuration automatique de documents dans les corpus semi structurés hétérogènes ”, Extraction et Gestion de Connaissances (EGC'05), vol. RNTI-E-3, Paris, France, pp. 227-238, (RNTI) (2005)
2004
L. Denoyer : “Apprentissage et inférence statistique dans les bases de documents structurés : Application aux corpus de documents textuels ”, أطروحة, مناقـشـة مـذكـرة 15/12/2004, رئاسـة البـحث Gallinari, Patrick (2004)L. Denoyer, P. Gallinari : “Bayesian Network Model for Semi-Structured Document Classification ”, Information Processing and Management, vol. 40 (5), pp. 807-827, (Elsevier) (2004)L. Denoyer, G. Wisniewski, P. Gallinari : “Document structure matching for heterogeneous corpora ”, SIGIR 2004 workshop on XML and Information Retrieval, Sheffield, United Kingdom (2004)L. Denoyer, P. Gallinari : “Semi Structured Document Classification ”, chapter in Encyclopedia Of Data Warehousing And Mining, (Information Science), (ISBN: 9781591405573) (2004)L. Denoyer, P. Gallinari : “Un modèle de mixture de Modèles Génératifs pour les Documents Structurés Multimedia ”, Document numérique - Revue des sciences et technologies de l'information. Série Document numérique, vol. 8 (3), pp. 35-54, (Hermès) (2004)
2003
L. Denoyer, J.‑N. Vittaut, P. Gallinari, S. Brunessaux, S. Brunessaux : “Structured Multimedia Document Classification ”, ACM Document Engeneering, Grenoble, France, pp. 153-160, (ACM) (2003)L. Denoyer, P. Gallinari : “Using belief networks and Fisher kernels for structured document classification ”, PKDD 2003 - 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, vol. 2838, Lecture Notes in Computer Science, Cavtat-Dubrovnik, Croatia, pp. 120-131, (Springer) (2003)L. Denoyer, P. Gallinari : “A belief networks-based generative model for structured documents. An application to the XML categorization ”, MLDM 2003 - Third International Conference on Machine Learning and Data Mining in Pattern Recognition, vol. 2734, Lecture Notes in Computer Science, Leipzig, Germany, pp. 328-342, (Springer) (2003)H.‑T. Vu, L. Denoyer, P. Gallinari : “Un modèle statistique pour la classification de documents structurés ”, Journées francophones d'Extraction et de Gestion des Connaissances (EGC 2003), Lyon, France (2003)
2002
2001
