Theodore Papamarkou | DeepAI

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∙ 04/06/2023

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry

Bayesian inference in deep neural networks is challenging due to the hig...

0 Jonas Gregor Wiese, et al. ∙

∙ 08/24/2022

The premise of approximate MCMC in Bayesian deep learning

This paper identifies several characteristics of approximate MCMC in Bay...

0 Theodore Papamarkou, et al. ∙

∙ 06/01/2022

Higher-Order Attention Networks

This paper introduces higher-order attention networks (HOANs), a novel c...

0 Mustafa Hajij, et al. ∙

∙ 01/25/2022

The Inverse Problem for Controlled Differential Equations

We study the problem of constructing the control driving a controlled di...

0 Anastasia Papavasiliou, et al. ∙

∙ 12/01/2021

Mixed neural network Gaussian processes

This paper makes two contributions. Firstly, it introduces mixed composi...

0 Alexey Lindo, et al. ∙

∙ 12/30/2020

Bayesian state space modelling for COVID-19: with Tennessee and New York case studies

We develop a Bayesian inferential framework for the spread of COVID-19 u...

0 Adam Spannaus, et al. ∙

∙ 08/18/2020

Bayesian neural networks and dimensionality reduction

In conducting non-linear dimensionality reduction and feature learning, ...

21 Deborshee Sen, et al. ∙

∙ 06/04/2020

Hidden Markov models are recurrent neural networks: A disease progression modeling application

Hidden Markov models (HMMs) are commonly used for sequential data modeli...

0 Matt Baucum, et al. ∙

∙ 03/06/2020

Automated detection of pitting and stress corrosion cracks in used nuclear fuel dry storage canisters using residual neural networks

Nondestructive evaluation methods play an important role in ensuring com...

1 Theodore Papamarkou, et al. ∙

∙ 01/03/2020

Wide Neural Networks with Bottlenecks are Deep Gaussian Processes

There is recently much work on the "wide limit" of neural networks, wher...

0 Devanshu Agrawal, et al. ∙

∙ 10/15/2019

Challenges in Bayesian inference via Markov chain Monte Carlo for neural networks

Markov chain Monte Carlo (MCMC) methods and neural networks are instrume...

0 Theodore Papamarkou, et al. ∙

∙ 07/19/2019

Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem

Random variables and their distributions are a central part in many area...

0 Mathieu Besançon, et al. ∙

∙ 07/30/2018

The Efficiency of Geometric Samplers for Exoplanet Transit Timing Variation Models

Transit timing variations (TTVs) are a valuable tool to determine the ma...

0 Noah W. Tuchow, et al. ∙

∙ 08/29/2016

On the Computational Complexity of Geometric Langevin Monte Carlo

Manifold Markov chain Monte Carlo algorithms have been introduced to sam...

0 Theodore Papamarkou, et al. ∙

∙ 07/26/2016

Forward-Mode Automatic Differentiation in Julia

We present ForwardDiff, a Julia package for forward-mode automatic diffe...

0 Jarrett Revels, et al. ∙

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