Article-Journal

Approximate blocked Gibbs sampling for Bayesian neural networks
In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample …
Theodore Papamarkou
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Depth-2 neural networks under a data-poisoning attack
In this work, we study the possibility of defending against data-poisoning attacks while training a shallow neural network in a …
Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou
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A random persistence diagram generator
Topological data analysis (TDA) studies the shape patterns of data. Persistent homology is a widely used method in TDA that summarizes …
Theodore Papamarkou, Farzana Nasrin, Austin Lawson, Na Gong, Orlando Rios, Vasileios Maroulas
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Challenges in Markov chain Monte Carlo for Bayesian neural networks
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews …
Theodore Papamarkou, Jacob Hinkle, M. Todd Young, David Womble
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Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling
The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the …
Adam Spannaus, Theodore Papamarkou, Samantha Erwin, J. Blair Christian
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Distributions.jl: definition and modeling of probability distributions in the JuliaStats ecosystem
Random variables and their distributions are a central part in many areas of statistical methods. The Distributions.jl package provides …
Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, John Pearson
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Geometric adaptive Monte Carlo in random environment
Manifold Markov chain Monte Carlo algorithms have been introduced to sample more effectively from challenging target densities …
Theodore Papamarkou, Alexey Lindo, Eric B. Ford
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Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator …
Theodore Papamarkou, Hayley Guy, Bryce Kroencke, Jordan Miller, Preston Robinette, Daniel Schultz, Jacob Hinkle, Laura Pullum, Catherine Schuman, Jeremy Renshaw, Stylianos Chatzidakis
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Wide neural networks with bottlenecks are deep Gaussian processes
There has recently been much work on the ‘wide limit’ of neural networks, where Bayesian neural networks (BNNs) are shown …
Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle
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The efficiency of geometric samplers for exoplanet transit timing variation models
Transit timing variations (TTVs) are a valuable tool to determine the masses and orbits of transiting planets in multiplanet systems. …
Noah W. Tuchow, Eric B. Ford, Theodore Papamarkou, Alexey Lindo
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