Deep Gaussian Process Emulation using Stochastic Imputation

Deep Gaussian Process Regression (DGPR)

A Gaussian Process Regression model is equivalent to an infinitely wide neural network with single hidden layer and similarly a DGP is a multi-layer neural network with multiple infinitely wide hidden layers [Neal, 1995]. DGPs employ a hierarchical structural of GP mappings and therefore are arguably more flexible, have a greater capacity to generalize, and are able to provide better predictive...

Scalable Gaussian Process Regression Using Deep Neural Networks

We propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function. We first pre-train the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then, we perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-...

Automatically Grading Learners’ English using a Deep Gaussian Process

Stochastic dynamic predictions using Gaussian process models for nanoparticle synthesis

Gaussian process modeling (also known as kriging) is an empirical modeling approach that has been widely applied in engineering for the approximation of deterministic functions, due to its flexibility and ability to interpolate observed data. Despite its statistical properties, Gaussian process models (GPM) have not been employed to describe the dynamics of stochastic systems with multiple outp...

Multiple Imputation Using Deep Denoising Autoencoders

Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness ...