Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Cholewa, Michał"'
Autor:
Cholewa, Michał, Romaszewski, Michał, Głomb, Przemysław, Kołodziej, Katarzyna, Gorawski, Michał, Koral, Jakub, Koral, Wojciech, Madej, Andrzej, Musioł, Kryspin
In this article, we propose an approach to leak localisation in a complex water delivery grid with the use of data from physical simulation (e.g. EPANET software). This task is usually achieved by a network of multiple water pressure sensors and anal
Externí odkaz:
http://arxiv.org/abs/2406.19900
Autor:
Romaszewski, Michał, Sekuła, Przemysław, Głomb, Przemysław, Cholewa, Michał, Kołodziej, Katarzyna
Large Language Models (LLMs) have shown exceptional performance in text processing. Notably, LLMs can synthesize information from large datasets and explain their decisions similarly to human reasoning through a chain of thought (CoT). An emerging ap
Externí odkaz:
http://arxiv.org/abs/2406.04926
Classification is one of the main areas of pattern recognition research, and within it, Support Vector Machine (SVM) is one of the most popular methods outside of field of deep learning -- and a de-facto reference for many Machine Learning approaches
Externí odkaz:
http://arxiv.org/abs/2111.02164
Autor:
Książek, Kamil, Głomb, Przemysław, Romaszewski, Michał, Cholewa, Michał, Grabowski, Bartosz, Búza, Krisztián
Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i.e. reconstructing the spectra of observed substances (endmembers) and their relative mixing fractions (abundances), which is neede
Externí odkaz:
http://arxiv.org/abs/2109.13748
The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms
Externí odkaz:
http://arxiv.org/abs/2008.10254
In the small target detection problem a pattern to be located is on the order of magnitude less numerous than other patterns present in the dataset. This applies both to the case of supervised detection, where the known template is expected to match
Externí odkaz:
http://arxiv.org/abs/1808.03513
Publikováno v:
In Forensic Science International March 2021 320
Publikováno v:
Quantum Inf Process 16: 101 (2017)
In this work, we extend the idea of Quantum Markov chains [S. Gudder. Quantum Markov chains. J. Math. Phys., 49(7), 2008] in order to propose Quantum Hidden Markov Models (QHMMs). For that, we use the notions of Transition Operation Matrices (TOM) an
Externí odkaz:
http://arxiv.org/abs/1503.08760
Autor:
Cholewa, Michał
Publikováno v:
Znak / The Sign. (777):46-53
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=855139
Autor:
Cholewa, Michał, Głomb, Przemysław
This paper presents a method of choosing number of states of a HMM based on number of critical points of the motion capture data. The choice of Hidden Markov Models(HMM) parameters is crucial for recognizer's performance as it is the first step of th
Externí odkaz:
http://arxiv.org/abs/1110.6287