157 citations · 2.9k across the 96 of their papers we have counts for
98 papers
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,…
3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels
Zhenzhen Weng, Alexander S. Gorban, Jingwei Ji +3
Training a 3D human keypoint detector from point clouds in a supervised manner requires large volumes of high quality labels. While it is relatively easy to capture large amounts o…
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…
Scikit-dimension: a Python package for intrinsic dimension estimation
Jonathan Bac, Evgeny M. Mirkes, Alexander N. Gorban +2
Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been s…
High-dimensional separability for one- and few-shot learning
Alexander N. Gorban, Bogdan Grechuk, Evgeny M. Mirkes +2
This work is driven by a practical question: corrections of Artificial Intelligence (AI) errors. These corrections should be quick and non-iterative. To solve this problem without…