Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Georgios Giannakis"'
Autor:
Gabriele Fambri, Marco Badami, Dimosthenis Tsagkrasoulis, Vasiliki Katsiki, Georgios Giannakis, Antonis Papanikolaou
Publikováno v:
Energies, Vol 13, Iss 19, p 5128 (2020)
The increasing resort to renewable energy distributed generation, which is needed to mitigate anthropogenic CO2 emissions, leads to challenges concerning the proper operation of electric distribution systems. As a result of the intrinsic nature of Re
Externí odkaz:
https://doaj.org/article/851cd9109ac84c5f8185e8ddfa5ce9dd
Autor:
Luciano De Tommasi, Hassan Ridouane, Georgios Giannakis, Kyriakos Katsigarakis, Georgios N Lilis, Dimitrios Rovas
Publikováno v:
Buildings, Vol 8, Iss 7, p 91 (2018)
This paper presents work undertaken as part of the European H2020 project OptEEmAL (Optimized Energy Efficient Design Platform for Refurbishment at District Level), toward development of a decision-support platform for building and district refurbish
Externí odkaz:
https://doaj.org/article/06ae4f93a3e04aa0bd6d7a309c5a46ba
Publikováno v:
2022 IEEE Power & Energy Society General Meeting (PESGM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee84a22166a016446e17a674f73886a8
https://doi.org/10.1109/pesgm48719.2022.9917161
https://doi.org/10.1109/pesgm48719.2022.9917161
Publikováno v:
Scopus-Elsevier
Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of ExtraFW is the pair of gradients leveraged per iteration, thanks to whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79315d52b4b3fb02f129c4ef42939535
https://doi.org/10.1609/aaai.v35i9.17012
https://doi.org/10.1609/aaai.v35i9.17012
Publikováno v:
Edge Caching for Mobile Networks ISBN: 9781839531224
Scopus-Elsevier
Scopus-Elsevier
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::609190a7954230f704ad03727f8ff4f7
https://doi.org/10.1049/pbte096e_ch19
https://doi.org/10.1049/pbte096e_ch19
Autor:
Georgios Giannakis
Τα τελευταία χρόνια ιδιαιτέρο ενδιαφέρον έχει παρουσιαστεί στον τομέα της ενεργειακήςαπόδοσης και εξοικονόμησης κτηρίων, καθώς η ταχε
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e55c5e3f772c4c88fd4285ed9c2b7bc
https://doi.org/10.12681/eadd/35981
https://doi.org/10.12681/eadd/35981
Publikováno v:
Scopus-Elsevier
Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documented merits in machine learning and signal processing applications. Unlike projection-based methods, momentum cannot improve the convergence rate of FW, in general. This limitatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::563d189aeb2a5caaff5eafddcbc2c158
Publikováno v:
Scopus-Elsevier
This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: \emph{contexts} and \emph{side observations}. In this setting, a learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::020a87087276bceeab5bb53c5dd3bda3
Publikováno v:
Scopus-Elsevier
The variance reduction class of algorithms including the representative ones, SVRG and SARAH, have well documented merits for empirical risk minimization problems. However, they require grid search to tune parameters (step size and the number of iter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d497ed09f99cb8002c51c1888d855bf
Publikováno v:
Publons
Scopus-Elsevier
Scopus-Elsevier
This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation. Simple rules are designed to detect slowly-varying gradients and,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::407cb349951a4b45ed399e179538f79a
http://arxiv.org/abs/1805.09965
http://arxiv.org/abs/1805.09965