Elif Vural

Department of Electrical and Electronics Engineering
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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...
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 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 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...
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...
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 ...
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...
Investigation of Stationarity for Graph Time Series Data Sets
Güneyi, Eylem Tuğçe; Vural, Elif (2021-01-07)
Graphs permit the analysis of the relationships in complex data sets effectively. Stationarity is a feature that facilitates the analysis and processing of random time signals. Since graphs have an irregular structure, the...
Estimating Partially Observed Graph Signals by Learning Spectrally Concentrated Graph Kernels
Turhan, Gulce; Vural, Elif (2021-01-01)
© 2021 IEEE.Graph models provide flexible tools for the representation and analysis of signals defined over irregular domains such as social or sensor networks. However, in real applications data observations are often not...
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