Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Medbouhi, A."'
Autor:
García-Castellanos, Alejandro, Medbouhi, Aniss Aiman, Marchetti, Giovanni Luca, Bekkers, Erik J., Kragic, Danica
We propose HyperSteiner -- an efficient heuristic algorithm for computing Steiner minimal trees in the hyperbolic space. HyperSteiner extends the Euclidean Smith-Lee-Liebman algorithm, which is grounded in a divide-and-conquer approach involving the
Externí odkaz:
http://arxiv.org/abs/2409.05671
Autor:
Medbouhi, Aniss Aiman, Marchetti, Giovanni Luca, Polianskii, Vladislav, Kravberg, Alexander, Poklukar, Petra, Varava, Anastasia, Kragic, Danica
Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbo
Externí odkaz:
http://arxiv.org/abs/2404.08608
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 1, Pp 199-236 (2023)
Variational auto-encoders (VAEs) are deep generative models used for unsupervised learning, however their standard version is not topology-aware in practice since the data topology may not be taken into consideration. In this paper, we propose two di
Externí odkaz:
https://doaj.org/article/6fe4d4f9cf01452ea81890add99e010f
Akademický článek
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Autor:
Ali Medbouhi, Fethi Benbelaïd, Nassim Djabou, Claire Beaufay, Mourad Bendahou, Joëlle Quetin-Leclercq, Aura Tintaru, Jean Costa, Alain Muselli
Publikováno v:
Molecules, Vol 24, Iss 14, p 2575 (2019)
The chemical composition of essential oils extracted from aerial parts of Eryngium campestre collected in 37 localities from Western Algeria was characterized using GC-FID and GC/MS analyses. Altogether, 52 components, which accounted for 70.1 to 86.
Externí odkaz:
https://doaj.org/article/c33a54f1b79e44e29716d1a67d081e22
Autor:
Ali Medbouhi, Aura Tintaru, Claire Beaufay, Jean-Valère Naubron, Nassim Djabou, Jean Costa, Joëlle Quetin-Leclercq, Alain Muselli
Publikováno v:
Molecules, Vol 23, Iss 12, p 3250 (2018)
The chemical composition of a hexanic extract of Eryngium campestre, obtained from its aerial parts, was investigated by GC-FID, GC/MS, HRMS, NMR and VCD analyses. The main compounds were germacrene D (23.6%), eudesma-4(15)-7-dien-1-β-ol (8.2%) and
Externí odkaz:
https://doaj.org/article/eec2b9de86d64952ab03b29fb6322642
Autor:
Medbouhi, Aniss Aiman
Variational Auto-Encoders (VAEs) are one of the most famous deep generative models. After showing that standard VAEs may not preserve the topology, that is the shape of the data, between the input and the latent space, we tried to modify them so that
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-309487
Autor:
Medbouhi, Aniss Aiman
Variational Auto-Encoders (VAEs) are one of the most famous deep generative models. After showing that standard VAEs may not preserve the topology, that is the shape of the data, between the input and the latent space, we tried to modify them so that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::bd98afbe8f154f5f8320f7324c1ef7b7
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-309487
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-309487
Publikováno v:
International Journal of Applied and Computational Mathematics. 7
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cce673cb31724571b30b54d2f8bd6a36
https://doi.org/10.1007/s40819-021-01173-5
https://doi.org/10.1007/s40819-021-01173-5
Publikováno v:
Machine Learning & Knowledge Extraction; Mar2023, Vol. 5 Issue 1, p199-236, 38p