Zobrazeno 1 - 10
of 315
pro vyhledávání: '"A. Gkillas"'
In this paper we propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. We address the challenges of data privacy and heterogeneity in autonomous vehicle environments by proposing a personali
Externí odkaz:
http://arxiv.org/abs/2411.04692
This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard approach of KalmanNet, a recurrent
Externí odkaz:
http://arxiv.org/abs/2411.05847
Autor:
Gkillas, Alexandros, Lalos, Aris
Anomaly and missing data constitute a thorny problem in industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy is achieved with the utilization of
Externí odkaz:
http://arxiv.org/abs/2411.03996
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract In 2015, all United Nations Member States adopted 17 Sustainable Development Goals (SDGs) for the 2030 agenda. Addressing the issue of employing alternative data sources for exploring aspects of utilizing said goals, this paper explores the
Externí odkaz:
https://doaj.org/article/69befb38c7174b5abcd9106658654903
In this paper, we propose a novel methodology for addressing the hyperspectral image deconvolution problem. This problem is highly ill-posed, and thus, requires proper priors (regularizers) to model the inherent spectral-spatial correlations of the H
Externí odkaz:
http://arxiv.org/abs/2306.06378
In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning
Externí odkaz:
http://arxiv.org/abs/2305.18646
Autor:
Nikos Piperigkos, Alexandros Gkillas, Gerasimos Arvanitis, Stavros Nousias, Aris Lalos, Apostolos Fournaris, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis, Konstantinos Moustakas
Publikováno v:
Frontiers in Robotics and AI, Vol 11 (2024)
Cyber–physical systems (CPSs) are evolving from individual systems to collectives of systems that collaborate to achieve highly complex goals, realizing a cyber–physical system of systems (CPSoSs) approach. They are heterogeneous systems comprisi
Externí odkaz:
https://doaj.org/article/f50e3791084a4c2c80cd4d0a66cb148f
Publikováno v:
IEEE Transactions on Image Processing ( Volume: 32) 22 February 2023 1513 - 1528
In this study, the problem of computing a sparse representation of multi-dimensional visual data is considered. In general, such data e.g., hyperspectral images, color images or video data consists of signals that exhibit strong local dependencies. A
Externí odkaz:
http://arxiv.org/abs/2203.15901
Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by ex
Externí odkaz:
http://arxiv.org/abs/2112.07617
Autor:
Gkillas, Konstantinos1 (AUTHOR) gillask@upatras.gr, Tantoula, Maria2 (AUTHOR) mtantoula@hmu.gr, Tzagarakis, Manolis3 (AUTHOR) tzagara@upatras.gr
Publikováno v:
Studies in Nonlinear Dynamics & Econometrics. Aug2024, p1. 29p. 5 Illustrations.