3 citations · 5 across the 7 of their papers we have counts for
8 papers
A quantum-assisted latent surrogate model for Bayesian inversion of heterogeneous material properties on NISQ hardware
Dewangga Alfarisy, Catherine F. Higham, Eky Febrianto
Bayesian inversion is widely used in engineering applications, including material characterisation, structural damage identification, and medical diagnostics. These inverse problem…
Compact Eye Tracking for VR/AR Displays via Deep Learned MicroLED Projection and Single-Pixel Sensing
Graeme E. Johnstone, Catherine F. Higham, Aisha Kanwal +5
Fast and accurate eye tracking in a virtual reality or augmented reality headset could lead to better display performance and enable novel methods of user interaction with the syst…
Actively Inferring Optimal Measurement Sequences
Catherine F. Higham, Paul Henderson, Roderick Murray-Smith
Measurement of a physical quantity such as light intensity is an integral part of many reconstruction and decision scenarios but can be costly in terms of acquisition time, invasio…
Deceptive Diffusion: Generating Synthetic Adversarial Examples
Lucas Beerens, Catherine F. Higham, Desmond J. Higham
We introduce the concept of deceptive diffusion -- training a generative AI model to produce adversarial images. Whereas a traditional adversarial attack algorithm aims to perturb…
Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
Catherine F. Higham, Desmond J. Higham, Peter Grindrod
Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative…
Testing a QUBO Formulation of Core-periphery Partitioning on a Quantum Annealer
Catherine F. Higham, Desmond J. Higham, Francesco Tudisco
We propose a new kernel that quantifies success for the task of computing a core-periphery partition for an undirected network. Finding the associated optimal partitioning may be e…
Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer
Catherine F. Higham, Adrian Bedford
We demonstrate the feasibility of framing a classically learned deep neural network as an energy based model that can be processed on a one-step quantum annealer in order to exploi…
Neural network identification of people hidden from view with a single-pixel, single-photon detector
Piergiorgio Caramazza, Alessandro Boccolini, Daniel Buschek +5
Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall…