Paper-Conference

Combinatorial complexes: bridging the gap between cell complexes and hypergraphs
Graph-based signal processing techniques have become essential for handling data in non-Euclidean spaces. However, there is a growing …
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzman-Saenz, Tolga Birdal, Michael T. Schaub
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Towards efficient MCMC sampling in Bayesian neural networks by exploiting symmetry
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density …
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
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Towards faster gene expression prediction via dimensionality reduction and feature selection
The majority of genes have a genetic component to their expression. Elastic nets have been shown effective at predicting …
Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Theodore Papamarkou
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Adapting random forests to predict obesity-associated gene expression
Random forests (RFs) are effective at predicting gene expression from genotype data. However, a comparison of RF regressors and …
Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Farideh Jalali-Najafabadi, Theodore Papamarkou
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Hidden Markov models as recurrent neural networks: an application to Alzheimer's disease
Hidden Markov models (HMMs) are commonly used for disease progression modeling when the true patient health state is not fully known. …
Matt Baucum, Anahita Khojandi, Theodore Papamarkou
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Multiphase MCMC sampling for parameter inference in nonlinear ordinary differential equations
Traditionally, ODE parameter inference relies on solving the system of ODEs and assessing fit of the estimated signal with the …
Alan Lazarus, Dirk Husmeier, Theodore Papamarkou
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Gender differences in equity crowdfunding
Online peer-to-peer investment platforms are increasingly popular venues for entrepreneurs and investors to engage in financial …
Emoke-Agnes Horvat, Theodore Papamarkou
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Optimal spreading sequences for chaos-based communication systems
As a continuation from [2], some higher-order statistical dependency aspects of chaotic spreading sequences used in communication …
Theodore Papamarkou, Anthony J. Lawrance
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Higher order dependency of chaotic maps
Some higher-order statistical dependency aspects of chaotic maps are presented. The autocorrelation function (ACF) of the mean-adjusted …
Anthony J. Lawrance, Theodore Papamarkou
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