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pro vyhledávání: '"Bouadi, Tassadit"'
Counterfactual explanations have become a mainstay of the XAI field. This particularly intuitive statement allows the user to understand what small but necessary changes would have to be made to a given situation in order to change a model prediction
Externí odkaz:
http://arxiv.org/abs/2304.12943
Publikováno v:
ECML PKDD 2022 - European Conference on Machine Learning and Knowledge Discovery in Databases., Sep 2022, Grenoble, France
Counterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modification of feature values that changes the predicted decision made by
Externí odkaz:
http://arxiv.org/abs/2212.10847
Akademický článek
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Facing an unknown situation, a person may not be able to firmly elicit his/her preferences over different alternatives, so he/she tends to express uncertain preferences. Given a community of different persons expressing their preferences over certain
Externí odkaz:
http://arxiv.org/abs/1708.03259
Autor:
Bouadi, Tassadit
Dans cette thèse, nous nous sommes intéressés à l'analyse des données de simulation issues du modèle agro-hydrologique TNT. Les objectifs consistaient à élaborer des méthodes d'analyse des résultats de simulation qui replacent l'utilisateur
Externí odkaz:
http://www.theses.fr/2013REN1S165/document
Autor:
Maasland, Troy, Pereira, João, Bastos, Diogo, de Goffau, Marcus, Nieuwdorp, Max, Zwinderman, Aeilko H., Levin, Evgeni, Kamp, Michael, Koprinska, Irena, Bibal, Adrien, Bouadi, Tassadit, Frénay, Benoît, Galárraga, Luis, Oramas, José, Adilova, Linara, Krishnamurthy, Yamuna, Kang, Bo, Largeron, Christine, Lijffijt, Jefrey, Viard, Tiphaine, Welke, Pascal, Ruocco, Massimiliano, Aune, Erlend, Gallicchio, Claudio, Schiele, Gregor, Pernkopf, Franz, Blott, Michaela, Fröning, Holger, Schindler, Günther, Guidotti, Riccardo, Monreale, Anna, Rinzivillo, Salvatore, Biecek, Przemyslaw, Ntoutsi, Eirini, Pechenizkiy, Mykola, Rosenhahn, Bodo, Buckley, Christopher, Cialfi, Daniela, Lanillos, Pablo, Ramstead, Maxwell, Verbelen, Tim, Ferreira, Pedro M., Andresini, Giuseppina, Malerba, Donato, Medeiros, Ibéria, Fournier-Viger, Philippe, Nawaz, M. Saqib, Ventura, Sebastian, Sun, Meng, Zhou, Min, Bitetta, Valerio, Bordino, Ilaria, Ferretti, Andrea, Gullo, Francesco, Ponti, Giovanni, Severini, Lorenzo, Ribeiro, Rita, Gama, João, Gavaldà, Ricard, Cooper, Lee, Ghazaleh, Naghmeh, Richiardi, Jonas, Roqueiro, Damian, Saldana Miranda, Diego, Sechidis, Konstantinos, Graça, Guilherme
Publikováno v:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases-International Workshops of ECML PKDD 2021, Proceedings, 1524 CCIS, 15-25
Communications in Computer and Information Science ISBN: 9783030937355
Communications in Computer and Information Science ISBN: 9783030937355
One of the most common pitfalls often found in high dimensional biological data sets are correlations between the features. This may lead to statistical and machine learning methodologies overvaluing or undervaluing these correlated predictors, while
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27f848a6982f703b1efe62ef0964e33a
https://doi.org/10.1007/978-3-030-93736-2_2
https://doi.org/10.1007/978-3-030-93736-2_2
Autor:
Villmann, Thomas, Staps, Daniel, Ravichandran, Jensun, Saralajew, Sascha, Biehl, Michael, Kaden, Marika, Bouadi, Tassadit, Fromont, Elisa, Hüllermeier, Eyke
Publikováno v:
Advances in Intelligent Data Analysis XX-20th International Symposium on Intelligent Data Analysis, IDA 2022, Proceedings, 354-364
STARTPAGE=354;ENDPAGE=364;TITLE=Advances in Intelligent Data Analysis XX-20th International Symposium on Intelligent Data Analysis, IDA 2022, Proceedings
Lecture Notes in Computer Science ISBN: 9783031013324
STARTPAGE=354;ENDPAGE=364;TITLE=Advances in Intelligent Data Analysis XX-20th International Symposium on Intelligent Data Analysis, IDA 2022, Proceedings
Lecture Notes in Computer Science ISBN: 9783031013324
We present a method, which allows to train a Generalized Matrix Learning Vector Quantization (GMLVQ) model for classification using data from several, maybe non-calibrated, sources without explicit transfer learning. This is achieved by using a siame
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e09606c31581e7869edbe1f3f01855c
https://doi.org/10.1007/978-3-031-01333-1_28
https://doi.org/10.1007/978-3-031-01333-1_28
Autor:
Mitton, Nathalie, Brossard, Ludovic, Bouadi, Tassadit, Garcia, Frédérick, Gautron, Romain, Hilgert, Nadine, Ienco, Dino, Largouët, Christine, Lutton, Evelyne, Masson, Véronique, Martin-Clouaire, Roger, Mugnier, Marie-Laure, Neveu, Pascal, Preux, Philippe, Raynal, Hélène, Rousset, Catherine, Termier, Alexandre, Bellon-Maurel, Véronique
Publikováno v:
Agriculture et numérique : un Livre Blanc d’Inria et INRAE pour construire les bases d’une agriculture numérique responsable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3631::1afd0fbc1dbbd7c7b460a2adb5cff124
http://agritrop.cirad.fr/601192/
http://agritrop.cirad.fr/601192/
Autor:
Brossard, Ludovic, Bellon Maurel, Véronique, Bisquert, Pierre, Bouadi, Tassadit, Garcia, Frédérick, Lenain, Roland, Labarthe, Pierre, Lutton, Evelyne, Maurel, Pierre, Mitton, Nathalie, Termier, Alexandre
Publikováno v:
Agriculture and Digital Technology: Getting the most out of digital technology to contribute to the transition to sustainable agriculture and food systems
Agriculture and Digital Technology: Getting the most out of digital technology to contribute to the transition to sustainable agriculture and food systems, 6, pp.112-134, 2022, White book Inria, 978-2-7261-1310-3
Agriculture and Digital Technology: Getting the most out of digital technology to contribute to the transition to sustainable agriculture and food systems, 6, pp.112-134, 2022, White book Inria, 978-2-7261-1310-3
Acknowledgements (contributions, proofreading, editing) – Lluis Miquel Pla Aragones, Isabelle Piot-Lepetit, Emmanuel Prados, Xavier Reboud; International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f87386731d3d244e58729687eb042cc0
https://hal.inrae.fr/hal-03667491/file/Chap6_Brossard_2022_White_Book.pdf
https://hal.inrae.fr/hal-03667491/file/Chap6_Brossard_2022_White_Book.pdf