research
∙
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...
share
research
∙
08/24/2022
The premise of approximate MCMC in Bayesian deep learning
This paper identifies several characteristics of approximate MCMC in Bay...
research
∙
06/01/2022
Higher-Order Attention Networks
This paper introduces higher-order attention networks (HOANs), a novel c...
research
∙
01/25/2022
The Inverse Problem for Controlled Differential Equations
We study the problem of constructing the control driving a controlled di...
research
∙
12/01/2021
Mixed neural network Gaussian processes
This paper makes two contributions. Firstly, it introduces mixed composi...
research
∙
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...
research
∙
08/18/2020
Bayesian neural networks and dimensionality reduction
In conducting non-linear dimensionality reduction and feature learning, ...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
08/29/2016
On the Computational Complexity of Geometric Langevin Monte Carlo
Manifold Markov chain Monte Carlo algorithms have been introduced to sam...
research
∙
07/26/2016
