Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Patra, Rhicheek"'
Autor:
Damaskinos, Georgios, Guerraoui, Rachid, Kermarrec, Anne-Marie, Nitu, Vlad, Patra, Rhicheek, Taiani, Francois
Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no ener
Externí odkaz:
http://arxiv.org/abs/2006.07273
Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software b
Externí odkaz:
http://arxiv.org/abs/1802.07928
Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback
Externí odkaz:
http://arxiv.org/abs/1711.06100
Recommenders have become widely popular in recent years because of their broader applicability in many e-commerce applications. These applications rely on recommenders for generating advertisements for various offers or providing content recommendati
Externí odkaz:
http://arxiv.org/abs/1711.05828
Akademický článek
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Autor:
Damaskinos, Georgios, Guerraoui, Rachid, Kermarrec, Anne-Marie, Nitu, Vlad-Tiberiu, Patra, Rhicheek, Taïani, François
Publikováno v:
Middleware 2020-ACM/IFIP 21st International Middleware conference
Middleware 2020-ACM/IFIP 21st International Middleware conference, Dec 2020, Delft / Virtual, Netherlands
Middleware 2020-ACM/IFIP 21st International Middleware conference, Dec 2020, Delft / Virtual, Netherlands
International audience; Existing approaches to distribute Generative Adversarial Networks (GANs) either (i) fail to scale for they typically put the two components of a GAN (the generator and the discriminator) on different machines, inducing signifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2ef558032a0dec4d7a85173d65c1a8d5
https://hal.inria.fr/hal-03043237/document
https://hal.inria.fr/hal-03043237/document
Autor:
Patra, Rhicheek
The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0eed519cb1cc7fba8b9a393674fb87a7
Akademický článek
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The ever growing amount of data available on the Internet calls for personalization. Yet the most effective personalization schemes such as those based on collaborative filtering (CF) are notoriously resource greedy. This paper presents HyRec an onli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=snsf_p3_pubs::9d401db6167f459782a5db8510b2a3a2
Publikováno v:
Proceedings of the VLDB Endowment; June 2017, Vol. 10 Issue: 10 p1070-1081, 12p
