Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Romain Hennequin"'
Autor:
Guillaume Salha, Romain Hennequin, Jean-Baptiste Remy, Manuel Moussallam, Michalis Vazirgiannis
Publikováno v:
Neural Networks. 142:1-19
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general framework to scale graph AE and VAE to large graphs with millions of no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cd0f741acdd213513800edf08240d9e
https://doi.org/10.1016/j.neunet.2021.04.015
https://doi.org/10.1016/j.neunet.2021.04.015
Publikováno v:
Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e183835a3af4ea05609a9b78030d2b7a
https://doi.org/10.1145/3503252.3531308
https://doi.org/10.1145/3503252.3531308
Autor:
Darius Afchar, Alessandro B. Melchiorre, Markus Schedl, Romain Hennequin, Elena V. Epure, Manuel Moussallam
Publikováno v:
AI Magazine; Vol. 43 No. 2: Summer 2022; 190-208
The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3d2d254a66c62cb16b8f06bb4720cec
Autor:
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas, Romain Hennequin, Michalis Vazirgiannis
Publikováno v:
Neural networks : the official journal of the International Neural Network Society. 153
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection problems where, according to recent and concurring experimental evaluati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1eb0f9f115ea58e96647121590ccf68
https://pubmed.ncbi.nlm.nih.gov/35816860
https://pubmed.ncbi.nlm.nih.gov/35816860
Publikováno v:
RecSys
Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods learn fixed user and item representations, which fails to capture the com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c49cd64e7df6daeec7b5687602322360
https://doi.org/10.1145/3460231.3474230
https://doi.org/10.1145/3460231.3474230
Autor:
Benjamin Chapus, Viet-Anh Tran, Guillaume Salha-Galvan, Michalis Vazirgiannis, Romain Hennequin
Publikováno v:
RecSys
On an artist’s profile page, music streaming services frequently recommend a ranked list of ”similar artists” that fans also liked. However, implementing such a feature is challenging for new artists, for which usage data on the service (e.g. s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02e41b37c24fbec356fa3641bcd03da6
https://doi.org/10.1145/3460231.3474252
https://doi.org/10.1145/3460231.3474252
Publikováno v:
ICASSP
Extensive works have tackled Language Identification (LID) in the speech domain, however their application to the singing voice trails and performances on Singing Language Identification (SLID) can be improved leveraging recent progresses made in oth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd7f1433a74d60957ee875af8147b1b7
https://doi.org/10.1109/icassp39728.2021.9414203
https://doi.org/10.1109/icassp39728.2021.9414203
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676575
ECML/PKDD (1)
ECML/PKDD (1)
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering. Graph AE, VAE and most of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::edd9e713daa0effa95e6586fa29db24f
https://doi.org/10.1007/978-3-030-67658-2_19
https://doi.org/10.1007/978-3-030-67658-2_19
Autor:
Michel Buffa, Franck Michel, Michael Fell, Alain Giboin, Maroua Tikat, Marco Winckler, Guillaume Pellerin, Johan Pauwels, Romain Hennequin, Elena Cabrio, Fabien Gandon
Publikováno v:
The Semantic Web ISBN: 9783030773847
ESWC
The Semantic Web. ESWC 2021. Lecture Notes in Computer Science, vol 12731.
The Semantic Web. ESWC 2021. Lecture Notes in Computer Science, vol 12731., pp.515-531, 2021, ⟨10.1007/978-3-030-77385-4_31⟩
ESWC
The Semantic Web. ESWC 2021. Lecture Notes in Computer Science, vol 12731.
The Semantic Web. ESWC 2021. Lecture Notes in Computer Science, vol 12731., pp.515-531, 2021, ⟨10.1007/978-3-030-77385-4_31⟩
International audience; Since 2017, the goal of the two-million song WASABI database has been to build a knowledge graph linking collected metadata (artists,discography, producers, dates, etc.) with metadata generated by the analysis of both the song
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3373bb052306932447501731ad7d2a14
https://doi.org/10.1007/978-3-030-77385-4_31
https://doi.org/10.1007/978-3-030-77385-4_31
Publikováno v:
Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale
Traitement Automatique des Langues Naturelles
Traitement Automatique des Langues Naturelles, 2021, Lille, France. pp.270-272
HAL
Traitement Automatique des Langues Naturelles
Traitement Automatique des Langues Naturelles, 2021, Lille, France. pp.270-272
HAL
International audience; Nous résumons nos travaux de recherche, présentés à la conférence EMNLP 2020 et portant sur la modélisation de la perception des genres musicaux à travers différentes cultures, à partir de représentations sémantique
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::734a236a63a978acc092176c52d0890d
https://hal.archives-ouvertes.fr/hal-03265881/file/206.pdf
https://hal.archives-ouvertes.fr/hal-03265881/file/206.pdf