dr Michał Cholewa

Email: 

Orcid ID: 

0000-0001-6549-1590

Stanowisko: 

adiunkt
Zespół badawczy

Pokój: 

4

Telefon: 

+48 32 2317319 w. 21

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Publikacje
Biblio RSS

In Press

1. Sekuła, P., M. Romaszewski, P. Głomb, M. Cholewa, and Ł. Pawela, "Quantum-aware Transformer model for state classification", International Conference on Computational Science 2025, In Press.

2025

2. Sekuła, P., N. Shayesteh, Q. He, S. Zahedian, R. Moscosso, and M. Cholewa, "Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach", Applied Sciences, vol. 15, issue 4, 2025.
3. Romaszewski, M., P. Sekuła, P. Głomb, M. Cholewa, and K. Kołodziej, "Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models", Journal of Artificial Intelligence and Soft Computing Research, vol. 15, issue 3, 03/2025.

2024

4. Kołodziej, K., M. Cholewa, M. Romaszewski, P. Głomb, W. Koral, and P. Sekuła, "Calibration of Water Demand Network Through a Machine Learned Digital Twin Model", 5th Polish Conference on Artificial Intelligence (PP-RAI 2024), 2024.
5. Kołodziej, K., M. Cholewa, P. Głomb, W. Koral, and M. Romaszewski, "Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials", arXiv preprint arXiv:2410.02772, 09/2024.
6. Cholewa, M., M. Romaszewski, P. Głomb, K. Kołodziej, M. Gorawski, J. Koral, W. Koral, A. Madej, and K. Musioł, "‘Just One More Sensor is Enough’ – Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors", IEEE Sensors Journal, 06/2024.
7. Romaszewski, M., P. Sekuła, P. Głomb, M. Cholewa, and K. Kołodziej, "Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models", arXiv preprint arXiv:2406.04926, 06/2024.

2023

8. Głomb, P., M. Cholewa, P. Foszner, and J. Bularz, "Continual learning of a time series model using a mixture of HMMs with application to the IoT fuel sensor verification", Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023, Warsaw, Poland, September 17-20, 2023, 2023.
9. Głomb, P., M. Cholewa, W. Koral, A. Madej, and M. Romaszewski, "Detection of emergent leaks using machine learning approaches", Water Supply, vol. 23, issue 6, 05/2023.

2022

10. Książek, K., P. Głomb, M. Romaszewski, M. Cholewa, B. Grabowski, and K. Buza, "Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation", 21st International Conference on Image Analysis and Processing, vol. 13231, Lecce, Italy, Springer, Cham, 05/2022.

2021

11. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, "A dataset for evaluating blood detection in hyperspectral images", Forensic Science International, vol. 320, 2021.

2020

12. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set], 2020.
13. Książek, K., M. Romaszewski, P. Głomb, B. Grabowski, and M. Cholewa, "Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks", Sensors, vol. 20, issue Recent Advances in Multi- and Hyperspectral Image Analysis, 11/2020.

2019

14. Głomb, P., K. Domino, M. Romaszewski, and M. Cholewa, "Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images", PP-RAI'2019, Wrocław, Poland, Wroclaw University of Science and Technology, pp. p. 121, 2019.
15. Cholewa, M., P. Głomb, and M. Romaszewski, "A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification", IEEE Geoscience and Remote Sensing Letters, vol. 16, pp. 467-471, March, 2019.

2018

16. Romaszewski, M., P. Głomb, and M. Cholewa, "Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data", Computer and Information Sciences: Springer International Publishing, 2018.
17. Głomb, P., M. Romaszewski, M. Cholewa, and K. Domino, "Application of hyperspectral imaging and Machine Learning methods for the detection of gunshot residue patterns", Forensic Science International, vol. 290, 07/2018.

2017

18. Cholewa, M., P. Gawron, P. Głomb, and D. Kurzyk, "Quantum hidden Markov models based on transition operation matrices", Quantum Information Processing, vol. 16, pp. 101, 2017.

2016

19. Cholewa, M., and A. Sochan, Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition, 2016.
20. Głomb, P., and M. Cholewa, "Performance of Interest Point Descriptors on Hyperspectral Images", Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, 2016.
21. Cholewa, M., and P. Głomb, "Two Stage SVM Classification for Hyperspectral Data", Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, 2016.
22. Romaszewski, M., P. Głomb, and M. Cholewa, "Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 121, pp. 60 - 76, 2016.
23. Gawron, P., M. Cholewa, and K. Kara, Rewolucja stanu - fantastyczne wprowadzenie do informatyki kwantowej, Gliwice, IITiS PAN, pp. 128, 2016.

2015

24. Głomb, P., and M. Cholewa, "Experimental Evaluation of Selected Approaches to Covariance Matrix Regularization", Artificial Intelligence and Soft Computing, vol. 9120: Springer International Publishing, pp. 391-401, 2015.

2013

25. Cholewa, M., Czołowe funkcje kuliste jako baza do aproksymacji sygnałów o ograniczonym paśmie: Instytut Informatyki Teoretycznej i Stosowanej PAN, 2013.

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Historia zmian

Data aktualizacji: 10/06/2022 - 10:42; autor zmian: Łukasz Zimny (lzimny@iitis.pl)