Search Results for author: Jonathan Bac

Found 6 papers, 4 papers with code

Domain Adaptation Principal Component Analysis: base linear method for learning with out-of-distribution data

1 code implementation28 Aug 2022 Evgeny M Mirkes, Jonathan Bac, Aziz Fouché, Sergey V. Stasenko, Andrei Zinovyev, Alexander N. Gorban

Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain).

Domain Adaptation

Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation

no code implementations30 Mar 2022 Qinghua Zhou, Alexander N. Gorban, Evgeny M. Mirkes, Jonathan Bac, Andrei Zinovyev, Ivan Y. Tyukin

Recent work by Mellor et al (2021) showed that there may exist correlations between the accuracies of trained networks and the values of some easily computable measures defined on randomly initialised networks which may enable to search tens of thousands of neural architectures without training.

Neural Architecture Search

Scikit-dimension: a Python package for intrinsic dimension estimation

1 code implementation6 Sep 2021 Jonathan Bac, Evgeny M. Mirkes, Alexander N. Gorban, Ivan Tyukin, Andrei Zinovyev

Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID).


Local intrinsic dimensionality estimators based on concentration of measure

no code implementations31 Jan 2020 Jonathan Bac, Andrei Zinovyev

In this paper, we introduce new local estimators of ID based on linear separability of multi-dimensional data point clouds, which is one of the manifestations of concentration of measure.

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