Elif Vural

E-mail

velif@metu.edu.tr

Department

Department of Electrical and Electronics Engineering

ORCID

0000-0002-5491-2588

Scopus Author ID

26645193800

Web of Science Researcher ID

AAZ-9275-2020

Graph domain adaptation with localized graph signal representations Pilavcı, Yusuf Yiğit; Güneyi, Eylem Tuğçe; Cengiz, Cemil; Vural, Elif (2024-11-01) 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 makin...
Learning Graph ARMA Processes From Time-Vertex Spectra Güneyi, Eylem Tuǧçe; Yaldiz, Berkay; Canbolat, Abdullah; Vural, Elif (2024-01-01) 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 gra...
Locally Stationary Graph Processes Canbolat, Abdullah; Vural, Elif (2024-01-01) 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 pr...
An Experimental Study of the Sample Complexity of Domain Adaptation Alan Uyarlamada Örnek Karmaşikliǧinin Deneysel Incelemesi Karaca, Huseyin; Akgül, Özlem; Arslan, Ömer Faruk; Aydemir, Atilla Can; Aydin, Firdevs Su; Ünsal, Enes Ata; Vural, Elif (2023-01-01) In this study, we experimentally investigate the sample complexity of semi supervised domain adaptation with deep neural networks. Sweeping the hyper-parameters of domain adaptation neural networks relying on the MMD dista...
Learning Graph Signal Representations with Narrowband Spectral Kernels Kar, Osman Furkan; Turhan, Gülce; Vural, Elif (2022-01-01) In this work, we study the problem of learning graph dictionary models from partially observed graph signals. We represent graph signals in terms of atoms generated by narrowband graph kernels. We formulate an optimization...
Estimation of Locally Stationary Graph Processes from Incomplete Realizations Canbolat, Abdullah; Vural, Elif (2022-01-01) Stationarity is a well-studied concept in signal processing and the concept of stationary random processes has been extended to graph domains in several recent works. Meanwhile, in many scenarios a globally stationary proc...
Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries Zamanda Deǧişen Graf Sinyallerinin Kestirimi için Graflarda Sözlük Öǧrenme Acar, Abdullah Burak; Vural, Elif (2022-01-01) We study the problem of estimating time-varying graph signals from missing observations. We propose a method based on learning graph dictionaries specified by a set of time-vertex kernels in the joint spectral domain. The ...
Learning Narrowband Graph Spectral Kernels for Graph Signal Estimation Çizge Sinyallerinin Dar Bantli Spektral Kernel Öǧrenimi ile Kestirimi Furkan Kar, Osman; Turhan, Gülce; Vural, Elif (2022-01-01) In this work, we study the problem of estimating graph signals from incomplete observations. We propose a method that learns the spectrum of the graph signal collection at hand by fitting a set of narrowband graph kernels ...
Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals Acar, Abdullah Burak; Vural, Elif (2022-01-01) In this work, we study the problem of learning time-vertex dictionaries for the modeling and estimation of time-varying graph signals. We consider a setting with a collection of partially observed time-varying graph signal...
A Theoretical Analysis of Multi-Modal Representation Learning with Regular Functions Vural, Elif (2021-01-07) Multi-modal data analysis methods often learn representations that align different modalities in a new common domain, while preserving the within-class compactness and within-modality geometry and enhancing the between-cla...

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