Výsledky vyhledávání - "Droit Arnaud"

Akademický článek

Validation of a 60K SNP chip for caribou (Rangifer tarandus) for use in wildlife forensics, conservation, and management

Autor: Trottier-Lavoie Mallorie, Prunier Julien, Poisson William, Carrier Alexandra, Gilbert Isabelle, Mastromonaco Gabriela, Albert Vicky, Cecilia Hernandez, Bourret Vincent, Taillon Joëlle, Droit Arnaud, Côté Steeve D., Robert Claude

Publikováno v: Forensic Science International: Animals and Environments, Vol 6, Iss , Pp 100093- (2024)

Large-scale genotyping platforms are currently being developed for several wild species. By querying thousands of polymorphic loci, genomics can be a useful ecological tool for describing and monitoring populations. Genomics is becoming increasingly

Externí odkaz: https://doaj.org/article/e2f5ceeb369e49b7954b390095054349

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Kernel KMeans clustering splits for end-to-end unsupervised decision trees

Autor: Ohl, Louis, Mattei, Pierre-Alexandre, Leclercq, Mickaël, Droit, Arnaud, Precioso, Frédéric

Trees are convenient models for obtaining explainable predictions on relatively small datasets. Although there are many proposals for the end-to-end construction of such trees in supervised learning, learning a tree end-to-end for clustering without

Externí odkaz: http://arxiv.org/abs/2402.12232

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Generalised Mutual Information: a Framework for Discriminative Clustering

Autor: Ohl, Louis, Mattei, Pierre-Alexandre, Bouveyron, Charles, Harchaoui, Warith, Leclercq, Mickaël, Droit, Arnaud, Precioso, Frédéric

In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely

Externí odkaz: http://arxiv.org/abs/2309.02858

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Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection

Autor: Ohl, Louis, Mattei, Pierre-Alexandre, Bouveyron, Charles, Leclercq, Mickaël, Droit, Arnaud, Precioso, Frédéric

Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters. While feature selection algorithms are often model-based through optimised m

Externí odkaz: http://arxiv.org/abs/2302.03391

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Generalised Mutual Information for Discriminative Clustering

Autor: Ohl, Louis, Mattei, Pierre-Alexandre, Bouveyron, Charles, Harchaoui, Warith, Leclercq, Mickaël, Droit, Arnaud, Precioso, Frederic

In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely

Externí odkaz: http://arxiv.org/abs/2210.06300

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