David Sharp

Implicit Gaussian process representation of vector fields over arbitrary latent manifolds

Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data. Recent works have extended GPs to model...

Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data

Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals. Detecting...

Hierarchical Gaussian Processes with Wasserstein-2 Kernels

Stacking Gaussian Processes severely diminishes the model's ability to detect outliers, which when combined with non-zero mean functions, further extrapolates low non-parametric variance to low...

Micro air vehicle based navigation aiding in degraded environments

Freeing product line architectures from execution dependencies

Pivotal Greenplum© for Kubernetes: Demonstration of Managing Greenplum Database on Kubernetes

Variable Aspects And Models For Reusable Component-Based Avionics Systems

Covert Robotics: Improving Covertness with Escapability and Non-Line-of-Sight Sensing