no code implementations • 12 Feb 2024 • Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms.
no code implementations • 16 Oct 2023 • Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro
Building upon this concept, we investigate NTKs and alignment in the context of graph neural networks (GNNs), where our analysis reveals that optimizing alignment translates to optimizing the graph representation or the graph shift operator in a GNN.
1 code implementation • NeurIPS 2023 • Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro
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" for an individual.
1 code implementation • 2 May 2023 • Saurabh Sihag, Gonzalo Mateos, Corey T. McMillan, Alejandro Ribeiro
To gauge the advantages offered by VNNs in neuroimaging data analysis, we focus on the task of "brain age" prediction using cortical thickness features.
no code implementations • 28 Oct 2022 • Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro
We have recently studied covariance neural networks (VNNs) that operate on sample covariance matrices using the architecture derived from graph convolutional networks, and we showed VNNs enjoy significant advantages over traditional data analysis approaches.
1 code implementation • 31 May 2022 • Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro
Moreover, our experiments on multi-resolution datasets also demonstrate that VNNs are amenable to transferability of performance over covariance matrices of different dimensions; a feature that is infeasible for PCA-based approaches.
no code implementations • 19 May 2022 • Max Wasserman, Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
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.
no code implementations • 6 May 2022 • Debarun Bhattacharjya, Saurabh Sihag, Oktie Hassanzadeh, Liza Bialik
Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora.
no code implementations • NeurIPS 2019 • Saurabh Sihag, Ali Tajer
Leveraging such side information can be abstracted as inferring structures of distinct graphical models that are {\sl partially} similar.
no code implementations • 19 Nov 2015 • 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.