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Found 5 papers, 0 papers with code
Complex Graph Laplacian Regularizer for Inferencing Grid States
In order to maintain stable grid operations, system monitoring and control processes require the computation of grid states (e. g. voltage magnitude and angles) at high granularity.
No-Box Attacks on 3D Point Cloud Classification
To this end, we define 14 point cloud features and use multiple linear regression to examine whether these features can be used for adversarial point prediction, and which combination of features is best suited for this purpose.
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction
Prediction of annual crop yields at a county granularity is important for national food production and price stability.
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment
Specifically, to articulate a sampling objective, we first assume a super-resolution (SR) method based on feature graph Laplacian regularization (FGLR) that reconstructs the original high-resolution PC, given 3D points chosen by a sampling matrix $\H$.
