4 papers
From Classical to Topological Neural Networks Under Uncertainty
Sarah Harkins Dayton, Layal Bou Hamdan, Ioannis D. Schizas +2
This chapter explores neural networks, topological data analysis, and topological deep learning techniques, alongside statistical Bayesian methods, for processing images, time seri…
Bayesian Topological Convolutional Neural Nets
Sarah Harkins Dayton, Hayden Everett, Ioannis Schizas +2
Convolutional neural networks (CNNs) have been established as the main workhorse in image data processing; nonetheless, they require large amounts of data to train, often produce o…
Complexity synchronization analysis of neurophysiological data: Theory and methods
Ioannis Schizas, Sabrina Sullivan, Scott E. Kerick +6
We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS) and complexity synchronization (CS) analysis to infer information tra…
Bayesian Sheaf Neural Networks
Patrick Gillespie, Layal Bou Hamdan, Ioannis Schizas +2
Equipping graph neural networks with a convolution operation defined in terms of a cellular sheaf offers advantages for learning expressive representations of heterophilic graph da…