1 code implementation • 31 Mar 2024 • Uladzislau Yorsh, Martin Holeňa, Ondřej Bojar, David Herel
Transformers have revolutionized deep learning in numerous fields, including natural language processing, computer vision, and audio processing.
no code implementations • 21 Sep 2023 • Lukáš Korel, Petr Pulc, Jiří Tumpach, Martin Holeňa
This paper provides an insight into the possibility of scene recognition from a video sequence with a small set of repeated shooting locations (such as in television series) using artificial neural networks.
no code implementations • 17 Sep 2023 • Lukáš Korel, Alexander S. Behr, Norbert Kockmann, Martin Holeňa
This paper provides an insight into the possibility of how to find ontologies most relevant to scientific texts using artificial neural networks.
no code implementations • 11 Feb 2022 • Zbyněk Pitra, Jan Koza, Jiří Tumpach, Martin Holeňa
Surrogate modeling has become a valuable technique for black-box optimization tasks with expensive evaluation of the objective function.
no code implementations • 28 Nov 2014 • Lukáš Bajer, Martin Holeňa
Outline of several strategies for using Gaussian processes as surrogate models for the covariance matrix adaptation evolution strategy (CMA-ES).