Search Results for author: Saurabh Sihag

Found 10 papers, 3 papers with code

Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks

no code implementations12 Feb 2024 Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro

Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms.

Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs

no code implementations16 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.

Time Series Time Series Prediction

Explainable Brain Age Prediction using coVariance Neural Networks

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.

Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features

1 code implementation2 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.

Predicting Brain Age using Transferable coVariance Neural Networks

no code implementations28 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.

coVariance Neural Networks

1 code implementation31 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.

Learning Graph Structure from Convolutional Mixtures

no code implementations19 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.

Graph Learning Link Prediction

Summary Markov Models for Event Sequences

no code implementations6 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.

Time Series Time Series Analysis

Structure Learning with Side Information: Sample Complexity

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.

Faster method for Deep Belief Network based Object classification using DWT

no code implementations19 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.

General Classification

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