12 citations · 24 across the 10 of their papers we have counts for
10 papers
Low-power analogue neural networks with trainable nonlinear connections for continuous control
Ian T. Vidamour, Fernando Aguirre, Thomas J. Hayward +13
Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear device responses to act as sc…
Left shifting analysis of Human-Autonomous Team interactions to analyse risks of autonomy in high-stakes AI systems
Ben Larwood, Oliver J. Sutton, Callum Cockburn
Developing high-stakes autonomous systems that include Artificial Intelligence (AI) components is complex; the consequences of errors can be catastrophic, yet it is challenging to…
Staining and locking computer vision models without retraining
Oliver J. Sutton, Qinghua Zhou, George Leete +2
We introduce new methods of staining and locking computer vision models, to protect their owners' intellectual property. Staining, also known as watermarking, embeds secret behavio…
Improving regional weather forecasts with neural interpolation
James Jackaman, Oliver Sutton
In this paper we design a neural interpolation operator to improve the boundary data for regional weather models, which is a challenging problem as we are required to map multi-sca…
Stealth edits to large language models
Oliver J. Sutton, Qinghua Zhou, Wei Wang +4
We reveal the theoretical foundations of techniques for editing large language models, and present new methods which can do so without requiring retraining. Our theoretical insight…
Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees
Ivan Y. Tyukin, Tatiana Tyukina, Daniel van Helden +6
We present a new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees. These AI correctors are auxiliary map…
Relative intrinsic dimensionality is intrinsic to learning
Oliver J. Sutton, Qinghua Zhou, Alexander N. Gorban +1
High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just…
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
Alexander Bastounis, Alexander N. Gorban, Anders C. Hansen +5
In this work, we assess the theoretical limitations of determining guaranteed stability and accuracy of neural networks in classification tasks. We consider classical distribution-…
How adversarial attacks can disrupt seemingly stable accurate classifiers
Oliver J. Sutton, Qinghua Zhou, Ivan Y. Tyukin +3
Adversarial attacks dramatically change the output of an otherwise accurate learning system using a seemingly inconsequential modification to a piece of input data. Paradoxically,…
Towards a mathematical understanding of learning from few examples with nonlinear feature maps
Oliver J. Sutton, Alexander N. Gorban, Ivan Y. Tyukin
We consider the problem of data classification where the training set consists of just a few data points. We explore this phenomenon mathematically and reveal key relationships bet…