34 citations · 36 across the 6 of their papers we have counts for
6 papers · 1 filter
Training Discrete Energy-Based Models with Energy Discrepancy
Tobias Schröder, Zijing Ou, Yingzhen Li +1
Training energy-based models (EBMs) on discrete spaces is challenging because sampling over such spaces can be difficult. We propose to train discrete EBMs with energy discrepancy…
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Tobias Schröder, Zijing Ou, Jen Ning Lim +3
Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them. We propose…
Meta-models for transfer learning in source localisation
Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross +2
In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent…
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy
Xing Liu, Andrew B. Duncan, Axel Gandy
Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests. It can be applied even when the target distribution has an unknown normalising…
Batch Bayesian Optimization via Particle Gradient Flows
Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis +1
Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a…
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning
L. A. Bull, D. Di Francesco, M. Dhada +5
A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilising an interpretable hierarchical Bayesian ap…