The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning

no code implementations • 13 Sep 2023 • Alexander Bastounis, Alexander N. Gorban, Anders C. Hansen, Desmond J. Higham, Danil Prokhorov, Oliver Sutton, Ivan Y. Tyukin, Qinghua Zhou

We consider classical distribution-agnostic framework and algorithms minimising empirical risks and potentially subjected to some weights regularisation.

Learning from few examples with nonlinear feature maps

no code implementations • 31 Mar 2022 • Ivan Y. Tyukin, Oliver Sutton, Alexander N. Gorban

In this work we consider the problem of data classification in post-classical settings were the number of training examples consists of mere few data points.