Real-Time Multimodal Emotion Recognition in Conversation for Multi-Party Interactions

Autor: Sandratra Rasendrasoa, Alexandre Pauchet, Julien Saunier, Sébastien Adam

Publikováno v: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f8d2cb97c2abc95b38b9205cd1e9f1c2
https://doi.org/10.1145/3536221.3556601

Data extraction and matching The EurHisFirm experience

Autor: Sébastien Adam, Jan Annaert, Frans Buelens, Coüasnon, Bertrand B., Boris Cule, Amaury de Vicq, Camille Guerry, Pierre-Cyrille Hautcoeur, Thierry Paquet, Andres Rojas Camacho, Iwan Le Floch, Aurélie Lemaitre, Pantelis Karapanagiotis, Johan Poukens, Angelo Riva

Publikováno v: Methodological Advances in the Extraction and Analysis of Historical Data
Methodological Advances in the Extraction and Analysis of Historical Data, Kellogg School of Management-Northwestern University, Dec 2021, Chicago/Virtual, United States
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International audience This paper reports results from the design phase of EurHisFirm. Its goal is to integrate isolated and badly accessible financial data sets on 19 th and 20 th century European companies so that users can query the data as if the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::d737e0319f3943ff2cc88c570924f89d
https://hal.science/hal-03828381

Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective

Autor: Muhammet Balcilar, Renton Guillaume, Pierre Héroux, Benoit Gaüzère, Sébastien Adam, Paul Honeine

Publikováno v: Proceedings of the International Conference on Learning Representations (ICLR)
Proceedings of the International Conference on Learning Representations (ICLR), May 2021, Vienna, Austria
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International audience; In the recent literature of Graph Neural Networks (GNN), the expressive power of models has been studied through their capability to distinguish if two given graphs are isomorphic or not. Since the graph isomorphism problem is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::e745d87ca601e932bcbdcf3e73f9ebbc
https://hal-normandie-univ.archives-ouvertes.fr/hal-03135633

Subgraph spotting in graph representations of comic book images

Autor: Anjan Dutta, Sébastien Adam, Jean-Marc Ogier, Muhammad Muzzamil Luqman, Christophe Rigaud, Pierre Héroux, Pasquale Foggia, Josep Lladós, Jean-Christophe Burie, Clément Guérin, Thanh-Nam Le

Publikováno v: Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2018, 112, pp.118-124. ⟨10.1016/j.patrec.2018.06.017⟩

Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e1400a9e476c6445f677086804267f5
https://doi.org/10.1016/j.patrec.2018.06.017

When Spectral Domain Meets Spatial Domain in Graph Neural Networks

Autor: Muhammet, Balcilar, Guillaume, Renton, Pierre, Héroux, Benoit, Gaüzère, Sébastien, Adam, Honeine, Paul

Publikováno v: Thirty-seventh International Conference on Machine Learning (ICML 2020)-Workshop on Graph Representation Learning and Beyond (GRL+ 2020)
Thirty-seventh International Conference on Machine Learning (ICML 2020)-Workshop on Graph Representation Learning and Beyond (GRL+ 2020), Jul 2020, Vienna, Austria

International audience; Convolutional Graph Neural Networks (Con-vGNNs) are designed either in the spectral domain or in the spatial domain. In this paper, we provide a theoretical framework to analyze these neural networks, by deriving some equivale

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c8e772ee4bb089c1e20f45dcfbe673a9
https://hal-normandie-univ.archives-ouvertes.fr/hal-03088374