2 citations · 4 across the 12 of their papers we have counts for
4 papers · 1 filter
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…