34 citations · 58 across the 10 of their papers we have counts for
11 papers
L-VAE: Variational Auto-Encoder with Learnable Beta for Disentangled Representation
Hazal Mogultay Ozcan, Sinan Kalkan, Fatos T. Yarman-Vural
In this paper, we propose a novel model called Learnable VAE (L-VAE), which learns a disentangled representation together with the hyperparameters of the cost function. L-VAE can b…
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…
Just Noticeable Difference for Machines to Generate Adversarial Images
Adil Kaan Akan, Mehmet Ali Genc, Fatos T. Yarman Vural
One way of designing a robust machine learning algorithm is to generate authentic adversarial images which can trick the algorithms as much as possible. In this study, we propose a…
On the Brain Networks of Complex Problem Solving
Abdullah Alchihabi, Omer Ekmekci, Baran B. Kivilcim +2
Complex problem solving is a high level cognitive process which has been thoroughly studied over the last decade. The Tower of London (TOL) is a task that has been widely used to s…
Modeling Brain Networks with Artificial Neural Networks
Baran Baris Kivilcim, Itir Onal Ertugrul, Fatos T. Yarman Vural
In this study, we propose a neural network approach to capture the functional connectivities among anatomic brain regions. The suggested approach estimates a set of brain networks,…
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
Arash Rahnama, Abdullah Alchihabi, Vijay Gupta +2
The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive…
Energy Saving Additive Neural Network
Arman Afrasiyabi, Ozan Yildiz, Baris Nasir +2
In recent years, machine learning techniques based on neural networks for mobile computing become increasingly popular. Classical multi-layer neural networks require matrix multipl…
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…
Zero-Resource Translation with Multi-Lingual Neural Machine Translation
Orhan Firat, Baskaran Sankaran, Yaser Al-Onaizan +2
In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation.…
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding
Itir Onal, Mete Ozay, Eda Mizrak +2
We represent the sequence of fMRI (Functional Magnetic Resonance Imaging) brain volumes recorded during a cognitive stimulus by a graph which consists of a set of local meshes. The…
Mesh Learning for Classifying Cognitive Processes
Mete Ozay, Ilke Öztekin, Uygar Öztekin +1
A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information…