AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Exploiting meaningful latent structures from data to solve downstream tasks is a fundamental challenge in signal processing and machine learning. While Principal Component Analysis (PCA) and coVariance Neural Networks (VNNs) successfully leverage the covariance matrix to process data, they inherently capture both direc...
The Kronecker coVariance Neural Network (KVNN) is introduced, a temporal graph neural network that represents the spatiotemporal covariance matrix via a sum of Kronecker products where spatial and temporal dependencies are decoupled, ultimately addressing all of ST-PCA's limitations.
An unoriented TSP (UTSP) framework that replaces oriented boundaries with unoriented incidence matrices is studied, and it is shown that unoriented incidence and Laplacian matrices between arbitrary simplicial levels admit graph-like spectral properties.
The conceptual insights facilitated by VNNs to the specific task of characterizing brain age gap for neurodegenerative conditions using neuroimaging datasets are elucidated, and how impact of these foundational advances permeates to principled designs and applications of learning methods across broad domains where cova...
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