Fatoş Tunay Yarman Vural

E-mail

yarman@metu.edu.tr

Department

ORCID

0000-0002-0510-9457

Scopus Author ID

6701826249

Web of Science Researcher ID

AAP-5605-2021

A concept-aware explainability method for convolutional neural networks Gurkan, Mustafa Kagan; Arica, Nafiz; Yarman Vural, Fatoş Tunay (2025-03-01) Although Convolutional Neural Networks (CNN) outperform the classical models in a wide range of Machine Vision applications, their restricted interpretability and their lack of comprehensibility in reasoning, generate many...
Visualization of Human Brain Network for the Analysis of Alzheimer's Disease Alzheimer Hastalığının Analizi için Beyin Ağlarının Görselleştirilmesi Değirmendereli, Gönül Günal; Aydın, Ulaş Sedat; Ahmadkhan, Abdulla; Türnüklü, Barış; Yarman Vural, Fatoş Tunay (2024-01-01) In this study, we propose a new model for visualizing brain networks of brain using functional Magnetic Resonance Imaging (fMRI). In this model, we estimate the probability density functions of the regions by considering t...
Editorial: Machine learning methods for human brain imaging Yarman Vural, Fatoş Tunay; Newman, Sharlene D.; Çukur, Tolga; Önal Ertugrul, Itır (2023-1-01)
DidEye: An Attempt for Defining and Incorporating Visual Intuition for Convolutional Neural Networks DidEye: Bir Görsel Sezgi Tanimlama Denemesi ve Evrişimsel Sinir Aǧlarina Uygulanmasi Koç, Robin; Yarman Vural, Fatoş Tunay (2023-01-01) Is it possible to formally define one of the important capabilities of human mind, the intuition, in a mathematical sense? Can we use this definition to develop more robust CNN (Convolutional Neural Networks) model? Throug...
An Analysis on Disentanglement in Machine Learning Makine Öǧrenmesinde Ayrişiklik Üzerine Bir Analiz Moğultay, Hazal; Kalkan, Sinan; Yarman Vural, Fatoş Tunay (2022-01-01) Learnt representations by Deep autoencoders is not capable of decomposing the complex information into simple notion. In other words, attributes of samples are entangled in the basis vectors spanning the learned space. Thi...
Just noticeable difference for machine perception and generation of regularized adversarial images with minimal perturbation Akan, Adil Kaan; Akbaş, Emre; Yarman Vural, Fatoş Tunay (2022-01-01) In this study, we introduce a measure for machine perception, inspired by the concept of Just Noticeable Difference (JND) of human perception. Based on this measure, we suggest an adversarial image generation algorithm, wh...
Texture Analysis by Deep Twin Networks for Paper Fraud Detection Ikiz Derin Aǧlarla Doku Analizi ile Evrak Sahteciliǧinin Tesbiti Ekiz, Ezgi; Şahin, Erol; Yarman Vural, Fatoş Tunay (2022-01-01) This study proposes a method to distinguish fake documents from the originals using the textural structures of the papers they are printed on. The study is based on observations showing that paper textures are different an...
Template-aligned Transfer Learning on Brain Decoding Problem Beyin Çözümleme Probleminde Şablon-Hizali Transfer Öǧrenme Eryol, Erkin; Yarman Vural, Fatoş Tunay (2022-01-01) Brain decoding involves a set of methods to estimate the brain activities that correspond to the brain signals, acquired via fMRI or similar techniques. Acquisition of fMRI data is a costly and hard process. Due to this, i...
Estimating Static and Dynamic Brain Networks by Kulback-Leibler Divergence from fMRI Data Degirmendereli, Gonul Gunal; Yarman Vural, Fatoş Tunay (2021-01-01) Representing brain activities by networks is very crucial to understand various cognitive states. This study proposes a novel method to estimate static and dynamic brain networks using Kulback-Leibler divergence. The sugge...
Gender classification using mesh networks on multiresolution multitask fMRI data Ertugrul, Itir Onal; Ozay, Mete; Yarman Vural, Fatoş Tunay (Springer Science and Business Media LLC, 2020-04-01) Brain connectivity networks have been shown to represent gender differences under a number of cognitive tasks. Recently, it has been conjectured that fMRI signals decomposed into different resolutions embed different types...

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