2 citations · 4 across the 12 of their papers we have counts for
7 papers · 1 filter
Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs
Saurabh Sihag, Andrea Cavallo, Elvin Isufi +2
This feature article provides an overview of the theoretical foundations for coVariance neural networks (VNNs), i.e., graph neural networks (GNNs) operating on covariance matrices…
Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing \textit{brain age gap} characterized by an elevated bra…
Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs
Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro
Neural tangent kernels (NTKs) provide a theoretical regime to analyze the learning and generalization behavior of over-parametrized neural networks. For a supervised learning task,…
Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features
Saurabh Sihag, Gonzalo Mateos, Corey T. McMillan +1
Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks. In our recent work, we have…
Predicting Brain Age using Transferable coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Corey McMillan +1
The deviation between chronological age and biological age is a well-recognized biomarker associated with cognitive decline and neurodegeneration. Age-related and pathology-driven…
Learning Graph Structure from Convolutional Mixtures
Max Wasserman, Saurabh Sihag, Gonzalo Mateos +1
Machine learning frameworks such as graph neural networks typically rely on a given, fixed graph to exploit relational inductive biases and thus effectively learn from network data…
coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Corey McMillan +1
Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the p…