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Found 10 papers, 6 papers with code
Robust and Conjugate Spatio-Temporal Gaussian Processes
State-space formulations allow for Gaussian process (GP) regression with linear-in-time computational cost in spatio-temporal settings, but performance typically suffers in the presence of outliers.
Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)
The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications.
Deep Optimal Sensor Placement for Black Box Stochastic Simulations
Selecting cost-effective optimal sensor configurations for subsequent inference of parameters in black-box stochastic systems faces significant computational barriers.
Modelling Global Trade with Optimal Transport
Global trade is shaped by a complex mix of factors beyond supply and demand, including tangible variables like transport costs and tariffs, as well as less quantifiable influences such as political and economic relations.
Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge
We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commuter train.
Meta-models for transfer learning in source localisation
In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent data.
Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset
Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models.
Grassmann Stein Variational Gradient Descent
Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo.
Energy-Based Models for Functional Data using Path Measure Tilting
Their ability to incorporate domain-specific choices and constraints into the structure of the model through composition make EBMs an appealing candidate for applications in physics, biology and computer vision and various other fields.
Density reconstruction from schlieren images through Bayesian nonparametric models
This study proposes a radically alternate approach for extracting quantitative information from schlieren images.
