activity
20162020
most citedEnergy Saving Additive Neural Network

2 citations · 2 across the 2 of their papers we have counts for

collaborators

7 papers

eess.IV2020

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…

q-bio.NC2018

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…

cs.CV2018

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,…

stat.ML2017

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…

cs.NE20172 cited

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…

cs.CL2016

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.…