May 31, 2022 · Motivated by this observation, we study a GNN architecture, called coVariance neural network (VNN), that operates on sample covariance matrices ...
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Oct 31, 2022 · We study a GNN architecture, called coVariance neural network (VNN), that operates on sample covariance matrices as graphs.
Sep 16, 2024 · A relational learning model that operates on the sample covariance matrix of the time series and leverages joint spatiotemporal convolutions to model the data.
Motivated by this observation, we study a GNN architecture, called coVariance neural network. (VNN), that operates on sample covariance matrices as graphs. We ...
In this paper, we propose a covariance neural network (CovNN), which replaces the original convolutional operation with our covariance algorithm, to make the ...
Apr 3, 2024 · Motivated by this observation, we study a GNN architecture, called coVariance neural network (VNN), that operates on sample covariance matrices ...
Consider a random vector X 2 Rm⇥1 , such that, its corresponding covariance matrix is given by C = E[(X E[X])(X E[X])T]. For a sample covariance matrix.
We propose a new architecture, called Covariant Compositional Neural Networks (CCNs), which are covariant to permutations rather than invariant.
In this paper, we leverage coVariance neural networks. (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction ...
Mar 28, 2024 · We show that VNNs exhibit transferability of performance (without re-training) over datasets whose covariance matrices converge to a limit object.