Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Huynh, Thu Van"'
Learning processes by exploiting restricted domain knowledge is an important task across a plethora of scientific areas, with more and more hybrid methods combining data-driven and model-based approaches. However, while such hybrid methods have been
Externí odkaz:
http://arxiv.org/abs/2307.02229
Publikováno v:
35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia
Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variables to predict a certain output. On the other hand, methods based on S
Externí odkaz:
http://arxiv.org/abs/2111.02218
Autor:
Huynh-Thu, Vân Anh, Geurts, Pierre
This paper presents a model-agnostic ensemble approach for supervised learning. The proposed approach is based on a parametric version of Random Subspace, in which each base model is learned from a feature subset sampled according to a Bernoulli dist
Externí odkaz:
http://arxiv.org/abs/2109.03099
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 July 2024 427
Autor:
Pierre, Nicolas, Huynh-Thu, Vân Anh, Baiwir, Dominique, Vieujean, Sophie, Bequet, Emeline, Reenaers, Catherine, Van Kemseke, Catherine, Salée, Catherine, Massot, Charlotte, Fléron, Maximilien, Mazzucchelli, Gabriel, Trzpiot, Lisette, Eppe, Gauthier, De Pauw, Edwin, Louis, Edouard, Meuwis, Marie-Alice
Publikováno v:
In Journal of Proteomics 30 June 2024 302
Akademický článek
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Autor:
Huynh-Thu, Vân Anh, Sanguinetti, Guido
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, reconstructing the structure of such networks has been a central computational problem
Externí odkaz:
http://arxiv.org/abs/1801.04087
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be
Externí odkaz:
http://arxiv.org/abs/1605.03848
Autor:
Marchand, Gwenaëlle, Huynh-Thu, Vân Anh, Kane, Nolan, Arribat, Sandrine, Varès, Didier, Rengel, David, Balzergue, Sandrine, Rieseberg, Loren, Vincourt, Patrick, Geurts, Pierre, Vignes, Matthieu, Langlade, Nicolas B.
Gene regulatory networks (GRN) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time scales. To identify patterns of molecular function and genetic diversity in
Externí odkaz:
http://arxiv.org/abs/1309.6066
Akademický článek
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