3 citations · 3 across the 4 of their papers we have counts for
4 papers
Modelling the Human Intuition to Complete the Missing Information in Images for Convolutional Neural Networks
Robin Koç, Fatoş T. Yarman Vural
In this study, we attempt to model intuition and incorporate this formalism to improve the performance of the Convolutional Neural Networks. Despite decades of research, ambiguitie…
Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification
Itir Onal Ertugrul, Mete Ozay, Fatos T. Yarman Vural
In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of…
Learning Deep Temporal Representations for Brain Decoding
Orhan Firat, Emre Aksan, Ilke Oztekin +1
Functional magnetic resonance imaging produces high dimensional data, with a less then ideal number of labelled samples for brain decoding tasks (predicting brain states). In this…
Discriminative Functional Connectivity Measures for Brain Decoding
Orhan Firat, Mete Ozay, Ilke Oztekin +1
We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resona…