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Found 6 papers, 0 papers with code
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning
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.
Efficient Low-Rank GNN Defense Against Structural Attacks
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.
GDM: Dual Mixup for Graph Classification with Limited Supervision
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.
Dual GNNs: Graph Neural Network Learning with Limited Supervision
In this paper, we propose a novel Dual GNN learning framework to address this challenge task.
On the Brain Networks of Complex Problem Solving
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.
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions.
