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Found 4 papers, 0 papers with code
Learning Interpretable Models for Coupled Networks Under Domain Constraints
This not only leads to a cleaner way of applying network constraints but also provides a more scalable solution when network connectivity is sparse.
DANR: Discrepancy-aware Network Regularization
Network regularization is an effective tool for incorporating structural prior knowledge to learn coherent models over networks, and has yielded provably accurate estimates in applications ranging from spatial economics to neuroimaging studies.
Autism Classification Using Brain Functional Connectivity Dynamics and Machine Learning
The goal of the present study is to identify autism using machine learning techniques and resting-state brain imaging data, leveraging the temporal variability of the functional connections (FC) as the only information.
Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data
Data mining practitioners are facing challenges from data with network structure.
