5 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…
Investigating the importance of county-level characteristics in opioid-related mortality across the United States
Andrew Deas, Adam Spannaus, Dakotah D. Maguire +3
The opioid crisis remains a critical public health challenge in the United States. Despite national efforts which reduced opioid prescribing rates by nearly 45\% between 2011 and 2…
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…
Functional Ultrasound Imaging Combined with Machine Learning for Whole-Brain Analysis of Drug-Induced Hemodynamic Changes
Jared Deighton, Shan Zhong, Kofi Agyeman +5
Functional ultrasound imaging (fUSI) is a cutting-edge technology that measures changes in cerebral blood volume (CBV) by detecting backscattered echoes from red blood cells moving…