Elif Vural

Department of Electrical and Electronics Engineering
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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...
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm
Kaya, Semih; Vural, Elif (2021-01-01)
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 ra...
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...
Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations
Guneyi, Eylem Tugce; Canbolat, Abdullah; Vural, Elif (2021-01-01)
© 2021 IEEE.We consider the problem of estimating time-varying graph signals with missing observations, which is of interest in many applications involving data acquisition on irregular topologies. We model time-varying gr...
Domain Adaptation on Graphs by Learning Aligned Graph Bases
Pilancı, Mehmet; Vural, Elif (Institute of Electrical and Electronics Engineers (IEEE), 2020-04-01)
A common assumption in semi-supervised learning is that the class label function has a slow variation on the data graph, while in many problems, the label function may vary abruptly in certain graph regions, resulting in h...
Mask Combination of Multi-Layer Graphs for Global Structure Inference
Bayram, Eda; Thanou, Dorina; Vural, Elif; Frossard, Pascal (2020-01-01)
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 c...
Multi-Modal Learning With Generalizable Nonlinear Dimensionality Reduction
KAYA, SEMİH; Vural, Elif (2019-08-26)
In practical machine learning settings, there often exist relations or links between data from different modalities. The goal of multimodal learning algorithms is to efficiently use the information available in different m...
Domain Adaptation on Graphs via Frequency Analysis
Pilancı, Mehmet; Vural, Elif (2019-08-22)
Classical machine learning algorithms assume the training and test data to be sampled from the same distribution, while this assumption may be violated in practice. Domain adaptation methods aim to exploit the information ...
Cross-modal Representation Learning with Nonlinear Dimensionality Reduction
KAYA, SEMİH; Vural, Elif (2019-08-22)
In many problems in machine learning there exist relations between data collections from different modalities. The purpose of multi-modal learning algorithms is to efficiently use the information present in different modal...
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