21 citations · 49 across the 11 of their papers we have counts for
11 papers
Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization
Veera Varuni Radhakrishnan, Chinthaka Dinesh, Qurat-ul-Ain Azim
Low-dose computed tomography (LDCT) reconstruction faces a critical tradeoff between reconstruction quality and resource requirements. While recent deep learning methods achieve st…
Complex Graph Laplacian Regularizer for Inferencing Grid States
Chinthaka Dinesh, Junfei Wang, Gene Cheung +1
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.…
Modeling Viral Information Spreading via Directed Acyclic Graph Diffusion
Chinthaka Dinesh, Gene Cheung, Fei Chen +2
Viral information like rumors or fake news is spread over a communication network like a virus infection in a unidirectional manner: entity conveys information to a neighbor $j…
Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks
Hanieh Naderi, Chinthaka Dinesh, Ivan V. Bajic +1
Adversarial attacks pose serious challenges for deep neural network (DNN)-based analysis of various input signals. In the case of three-dimensional point clouds, methods have been…
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction
Saghar Bagheri, Chinthaka Dinesh, Gene Cheung +1
Prediction of annual crop yields at a county granularity is important for national food production and price stability. In this paper, towards the goal of better crop yield predict…
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
Chinthaka Dinesh, Gene Cheung, Saghar Bagheri +1
A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. Howe…