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of 167
pro vyhledávání: '"Vural, Elif"'
In this paper, we propose an algorithm for downlink (DL) channel covariance matrix (CCM) estimation for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) communication systems with base station (BS) possessing a uniform
Externí odkaz:
http://arxiv.org/abs/2407.18865
Autor:
Canbolat, Abdullah, Vural, Elif
Stationary graph process models are commonly used in the analysis and inference of data sets collected on irregular network topologies. While most of the existing methods represent graph signals with a single stationary process model that is globally
Externí odkaz:
http://arxiv.org/abs/2309.01657
The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants.
Externí odkaz:
http://arxiv.org/abs/2302.06887
Publikováno v:
In Pattern Recognition November 2024 155
Autor:
Vural, Elif Hilal1 elif.vural@lokmanhekim.edu.tr, Granberg, Arzu Güneş2, Çengel, Atiye3, Dahl, Marja-Liisa4, Zengil, Hakan5
Publikováno v:
Gazi Medical Journal. 2024, Vol. 35 Issue 3, p325-331. 7p.
Autor:
Kaya, Semih, Vural, Elif
While many approaches exist in the literature to learn low-dimensional representations for data collections in multiple modalities, the generalizability of multi-modal nonlinear embeddings to previously unseen data is a rather overlooked subject. In
Externí odkaz:
http://arxiv.org/abs/2006.02330
Akademický článek
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In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we aim to estimate the target labels by making use of the similarity betw
Externí odkaz:
http://arxiv.org/abs/1911.02883
Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Alt
Externí odkaz:
http://arxiv.org/abs/1910.10114
Autor:
Vural, Elif
Traditional machine learning algorithms assume that the training and test data have the same distribution, while this assumption does not necessarily hold in real applications. Domain adaptation methods take into account the deviations in the data di
Externí odkaz:
http://arxiv.org/abs/1812.06944