12 citations · 21 across the 9 of their papers we have counts for
10 papers
Harnessing non-adversarial robustness in large language models
Qinghua Zhou, Ellina Aleshina, Andrey Lovyagin +6
The work presents an approach for addressing the challenge of robustness in Large Language Models (LLMs) to alterations and potential errors caused by semantically similar but text…
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…
StepProof: Step-by-step verification of natural language mathematical proofs
Xiaolin Hu, Qinghua Zhou, Bogdan Grechuk +1
Interactive theorem provers (ITPs) are powerful tools for the formal verification of mathematical proofs down to the axiom level. However, their lack of a natural language interfac…
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,…
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation
Qinghua Zhou, Alexander N. Gorban, Evgeny M. Mirkes +3
Finding best architectures of learning machines, such as deep neural networks, is a well-known technical and theoretical challenge. Recent work by Mellor et al (2021) showed that t…
Demystification of Few-shot and One-shot Learning
Ivan Y. Tyukin, Alexander N. Gorban, Muhammad H. Alkhudaydi +1
Few-shot and one-shot learning have been the subject of active and intensive research in recent years, with mounting evidence pointing to successful implementation and exploitation…