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Specific informations of {{{1}}}'s site:
- [[{{{1}}}:Hardware|Hardware]]
- [[{{{1}}}:Network|Network]]
- [[{{{1}}}:Storage|Storage]]
- [[{{{1}}}:External access|External access]]
- [[{{{1}}}:People|People]]
- [[{{{1}}}:Logs|Logs]]
- Administration
Direct access to resources for {{{1}}}'s site:
- reservation state and reservation history via OAR
- available resources via Ganglia
- critical services via Nagios
- opened bugs via BugZilla
- {{{1}}}-staff via mail
Shortcuts to global tools and informations:
- support procedures to report bugs or ask for enhancements
- global reservation state and global reservation history via OARgrid.
- registered users via phpLDAPadmin
Latest updated publications Five random publications that benefited from Grid'5000 (at least 2954 overall):
- Thomas Firmin, Pierre Boulet, El-Ghazali Talbi. Asynchronous Multi-fidelity Hyperparameter Optimization Of Spiking Neural Networks. International Conference on Neuromorphic Systems (ICONS 2024), Jul 2024, Washington, United States. hal-04781629 view on HAL pdf
- Ndeye-Emilie Mbengue. In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs. ESWC 2026 - 23rd European Semantic Web Conference, May 2026, Dubrovnik, Croatia. hal-05608081 view on HAL pdf
- Hee-Soo Choi, Priyansh Trivedi, Mathieu Constant, Karën Fort, Bruno Guillaume. Au-delà de la performance des modèles : la prédiction de liens peut-elle enrichir des graphes lexico-sémantiques du français ?. Actes de JEP-TALN-RECITAL 2024. 31ème Conférence sur le Traitement Automatique des Langues Naturelles, volume 1 : articles longs et prises de position, Jul 2024, Toulouse, France. pp.36-49. hal-04623008 view on HAL pdf
- Pierre-François Gimenez, Jérôme Mengin. Learning Conditional Preference Networks: an Approach Based on the Minimum Description Length Principle. IJCAI 2024 - 33rd International Joint Conference on Artificial Intelligence, Aug 2024, Jeju, South Korea. pp.3395-3403, 10.24963/ijcai.2024/376. hal-04572196 view on HAL pdf
- Mai Huong Do, Millian Poquet, Georges da Costa. FedE-ator : A framework for energy consumption analysis of federated learning in distributed systems. Compas’2025 : Parallélisme / Architecture/ Système, Jul 2025, Bordeaux, France. hal-05181877 view on HAL pdf
Latest updated experiments {{#experiments: 5|{{{1}}}}}
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