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Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions.

, , , , и . CoRR, (2022)

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Olivier Teste

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Olivier Coutand

 

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Interrogation de données structurellement hétérogènes dans les bases de données orientées documents., , , и . EGC, том E-34 из RNTI, стр. 155-166. Éditions RNTI, (2018)A Conceptual Model for Multidimensional Analysis of Documents, , , и . Conceptual Modeling - ER 2007, (2008)Node2Vec Stability: Preliminary Study to Ensure the Compatibility of Embeddings with Incremental Data Alignment., , , , , и . MEDI Workshops, том 2071 из Communications in Computer and Information Science, стр. 79-88. Springer, (2023)DECWA : Density-Based Clustering using Wasserstein Distance., , , и . CoRR, (2023)A New Accurate Clustering Approach for Detecting Different Densities in High Dimensional Data., , , и . DaWaK, том 12925 из Lecture Notes in Computer Science, стр. 167-179. Springer, (2021)KD-means: Clustering Method for Massive Data based on KD-tree., , и . DOLAP, том 2572 из CEUR Workshop Proceedings, стр. 26-35. CEUR-WS.org, (2020)AutoXAI: Un cadre pour sélectionner automatiquement la solution d'XAI la plus adaptée., , , , и . EGC, том E-39 из RNTI, стр. 491-498. Editions RNTI, (2023)AutoXAI: A Framework to Automatically Select the Most Adapted XAI Solution., , , , и . CIKM, стр. 315-324. ACM, (2022)An Automatic Schema-Instance Approach for Merging Multidimensional Data Warehouses., , , и . IDEAS, стр. 232-241. ACM, (2021)Identifying authoritative researchers in digital libraries using external a priori knowledge., , , и . SAC, стр. 1017-1022. ACM, (2017)