204 citations · 228 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
Exploring the impact of social stress on the adaptive dynamics of COVID-19: Typing the behavior of naïve populations faced with epidemics
Innokentiy Kastalskiy, Andrei Zinovyev, Evgeny Mirkes +2
In the context of natural disasters, human responses inevitably intertwine with natural factors. The COVID-19 pandemic, as a significant stress factor, has brought to light profoun…
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…
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…
Situation-based memory in spiking neuron-astrocyte network
Susanna Gordleeva, Yuliya A. Tsybina, Mikhail I. Krivonosov +4
Mammalian brains operate in a very special surrounding: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many…
Astrocytes mediate analogous memory in a multi-layer neuron-astrocytic network
Yuliya Tsybina, Innokentiy Kastalskiy, Mikhail Krivonosov +4
Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. Here we propose a mathematical model of spiking…
Principal Graphs and Manifolds
A. N. Gorban, A. Y. Zinovyev
In many physical, statistical, biological and other investigations it is desirable to approximate a system of points by objects of lower dimension and/or complexity. For this purpo…