5 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…
The Neuromorphic Supremacy
Yuliya Tsybina, Ivan Y. Tyukin, Alexander N. Gorban +3
Live neural systems demonstrate remarkable capabilities to learn new behavior and patterns from mere few examples and are known to operate robustly under severe sensory noise. Thes…
When fractional quasi p-norms concentrate
Ivan Y. Tyukin, Bogdan Grechuk, Evgeny M. Mirkes +1
Concentration of distances in high dimension is an important factor for the development and design of stable and reliable data analysis algorithms. In this paper, we address the fu…
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-…
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…