2 citations · 4 across the 12 of their papers we have counts for
15 papers
Brain Age Gap Progression in Individuals at Risk for Neurodegeneration
Jason Scheffel, Saurabh Sihag
Neuroimaging is a widely used modality for characterizing neurodegeneration in various health conditions. Brain age gap has emerged as a promising biomarker for monitoring brain he…
Covariance Eigenspace Provides Latent Attribution of Longitudinal Effects in Brain Age Gap
Jason Scheffel, Saurabh Sihag
Brain age gap is a promising machine learning (ML)-driven bio\-marker of accelerated biological aging and is derived from neuroimaging data. Recent works on coVariance neural netwo…
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…
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,…
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…
Explainable Brain Age Prediction using coVariance Neural Networks
Saurabh Sihag, Gonzalo Mateos, Corey McMillan +1
In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" f…
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…
Summary Markov Models for Event Sequences
Debarun Bhattacharjya, Saurabh Sihag, Oktie Hassanzadeh +1
Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora. We pr…
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…
Secure Estimation under Causative Attacks
Saurabh Sihag, Ali Tajer
This paper considers the problem of secure parameter estimation when the estimation algorithm is prone to causative attacks. Causative attacks, in principle, target decision-making…
Faster method for Deep Belief Network based Object classification using DWT
Saurabh Sihag, Pranab Kumar Dutta
A Deep Belief Network (DBN) requires large, multiple hidden layers with high number of hidden units to learn good features from the raw pixels of large images. This implies more tr…