2 citations · 2 across the 4 of their papers we have counts for
4 papers
Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective
Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
Neurodegeneration, characterized by the progressive loss of neuronal structure or function, is commonly assessed in clinical practice through reductions in cortical thickness or br…
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…
Towards a Foundation Model for Brain Age Prediction 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 brain age with respect to chronological age can reflect…
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,…