2 citations · 4 across the 8 of their papers we have counts for
6 papers · 1 filter
Channel-Attentive Graph Neural Networks
Tuğrul Hasan Karabulut, İnci M. Baytaş
Graph Neural Networks (GNNs) set the state-of-the-art in representation learning for graph-structured data. They are used in many domains, from online social networks to complex mo…
EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence
Ilkay Sikdokur, İnci M. Baytaş, Arda Yurdakul
Deep edge intelligence aims to deploy deep learning models that demand computationally expensive training in the edge network with limited computational power. Moreover, many deep…
Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation
Deniz Sezin Ayvaz, Inci M. Baytas
Alzheimer's disease is the most common cause of dementia that affects millions of lives worldwide. Investigating the underlying causes and risk factors of Alzheimer's disease is es…
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
Mengying Sun, Inci M. Baytas, Liang Zhan +2
Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroi…
Asynchronous Multi-Task Learning
Inci M. Baytas, Ming Yan, Anil K. Jain +1
Many real-world machine learning applications involve several learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a ce…
Similarity Learning via Adaptive Regression and Its Application to Image Retrieval
Qi Qian, Inci M. Baytas, Rong Jin +2
We study the problem of similarity learning and its application to image retrieval with large-scale data. The similarity between pairs of images can be measured by the distances be…