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Found 12 papers, 3 papers with code
Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
We provide a brief introduction to diffusion models for applied mathematicians and statisticians.
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks
We develop and assess algorithms for both targeted and untargeted attacks.
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
We consider classical distribution-agnostic framework and algorithms minimising empirical risks and potentially subjected to some weights regularisation.
How adversarial attacks can disrupt seemingly stable accurate classifiers
We introduce a simple generic and generalisable framework for which key behaviours observed in practical systems arise with high probability -- notably the simultaneous susceptibility of the (otherwise accurate) model to easily constructed adversarial attacks, and robustness to random perturbations of the input data.
Backward error analysis and the qualitative behaviour of stochastic optimization algorithms: Application to stochastic coordinate descent
Stochastic optimization methods have been hugely successful in making large-scale optimization problems feasible when computing the full gradient is computationally prohibitive.
Can We Rely on AI?
Over the last decade, adversarial attack algorithms have revealed instabilities in deep learning tools.
Adversarial Ink: Componentwise Backward Error Attacks on Deep Learning
We also study the use of a componentwise condition number to quantify vulnerability.
The Feasibility and Inevitability of Stealth Attacks
Such a stealth attack could be conducted by a mischievous, corrupt or disgruntled member of a software development team.
Node and Edge Nonlinear Eigenvector Centrality for Hypergraphs
Network scientists have shown that there is great value in studying pairwise interactions between components in a system.
Social and Information Networks Numerical Analysis Numerical Analysis Data Analysis, Statistics and Probability
On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems
We show that in both cases, i. e., in the case of an attack based on adversarial examples and in the case of a stealth attack, the dimensionality of the AI's decision-making space is a major contributor to the AI's susceptibility.
A Nonlinear Spectral Method for Core--Periphery Detection in Networks
We derive and analyse a new iterative algorithm for detecting network core--periphery structure.
Social and Information Networks Numerical Analysis Data Analysis, Statistics and Probability
Deep Learning: An Introduction for Applied Mathematicians
This article provides a very brief introduction to the basic ideas that underlie deep learning from an applied mathematics perspective.
