no code implementations • 6 Dec 2023 • Abdullah Alchihabi, Marzi Heidari, Yuhong Guo
Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the target domain, cross-domain few-shot learning (CDFSL) induces a very challenging adaptation problem.
no code implementations • 18 Sep 2023 • Abdullah Alchihabi, Qing En, Yuhong Guo
As a result, instead of using the dense adjacency matrix directly, ELR-GNN can learn a low-rank and sparse estimate of it in a simple, efficient and easy to optimize manner.
no code implementations • 18 Sep 2023 • Abdullah Alchihabi, Yuhong Guo
In this work, we propose a novel mixup-based graph augmentation method, Graph Dual Mixup (GDM), that leverages both functional and structural information of the graph instances to generate new labeled graph samples.
no code implementations • 29 Jun 2021 • Abdullah Alchihabi, Yuhong Guo
In this paper, we propose a novel Dual GNN learning framework to address this challenge task.
no code implementations • 10 Oct 2018 • Abdullah Alchihabi, Omer Ekmekci, Baran B. Kivilcim, Sharlene D. Newman, Fatos T. Yarman Vural
The network properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions.
no code implementations • 13 Aug 2017 • Arash Rahnama, Abdullah Alchihabi, Vijay Gupta, Panos Antsaklis, Fatos T. Yarman Vural
We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions.