Dr hab. inż. Przemysław Klęsk, prof. ZUT
From WikiZMSI
(Przekierowano z Dr hab. inż. Przemysław Klęsk)
| Pokój | 107 |
| telefon | 091 449 55 56 |
| pklesk@zut.edu.pl |
Spis treści |
[edytuj]
Publikacje i profil
[edytuj]
Repozytoria Github
- wszystkie: https://github.com/pklesk
- FastRealBoostBins: https://github.com/pklesk/fast_rboost_bins
- Home-Made Deep Learning: https://github.com/pklesk/hmdl
[edytuj]
Wybrane publikacje
- P. Klęsk (2024), FastRealBoostBins: An ensemble classifier for fast predictions implemented in Python via numba.jit and numba.cuda, SoftwareX, Elsevier, DOI: 10.1016/j.softx.2024.101644
- P. Klęsk (2024), Understanding the Flows of Signals and Gradients: A Tutorial on Algorithms Needed to Implement a Deep Neural Network from Scratch, Applied Sciences 14(21), 9972, Special Issue Advanced Digital Signal Processing and Its Applications
- P. Klęsk, M. Korzeń (2020), Can Boosted Randomness Mimic Learning Algorithms of Geometric Nature? Example of a Simple Algorithm That Converges in Probability to Hard-Margin SVM, IEEE Transactions on Neural Networks and Learning Systems, 32(9), pp. 3798-3818, DOI: 10.1109/TNNLS.2021.3059653
- D. Sychel, P. Klęsk, A. Bera (2020), Relaxed Per-Stage Requirements for Training Cascades of Classifiers, in 'European Conference on Artificial Intelligence, ECAI 2020', IOS Press, pp. 1523-1530
- A. Bera, P. Klęsk, D. Sychel (2019), Constant-time Calculation of Zernike Moments for Detection with Rotational Invariance, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(3), pp. 537-551
- P. Klęsk (2017), Constant-Time Fourier Moments for Face Detection - Can Accuracy of Haar-Like Features Be Beaten?, in 'International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, Zakopane, Poland', Lecture Notes in Computer Science book series (LNCS, volume 10245), Springer International Publishing, pp. 530-543
- P. Klęsk, A. Godziuk, M. Kapruziak, B. Olech (2015), Fast Analysis of C-scans From Ground Penetrating Radar via 3-D Haar-like Features With Application to Landmine Detection, IEEE Transactions on Geoscience and Remote Sensing, 53(7), pp. 3996-4009
- M. Korzeń, S. Jaroszewicz, P. Klęsk (2013), Logistic regression with weight grouping priors, Computational Statistics & Data Analysis, Elsevier, 64, pp. 281-298
- P. Klęsk, M. Korzeń (2011), Sets of approximating functions with finite Vapnik–Chervonenkis dimension for nearest-neighbors algorithms, Pattern Recognition Letters, Elsevier, 32(14), pp. 1882-1893
- P. Klęsk (2011), A Relationship between Cross-validation and Vapnik Bounds on Generalization of Learning Machines, in 'Proceedings of the 3rd International Conference on Agents and Artificial Intelligence', ICAART 2011, Rome, Italy, part 1, pp. 5-17
- P. Klęsk (2010), Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks, International Journal of Applied Mathematics and Computer Science, 20(3), pp. 525-544
- P. Klęsk (2008), Construction of a Neurofuzzy Network Capable of Extrapolating (and Interpolating) With Respect to the Convex Hull of a Set of Input Samples R^n, IEEE Transactions on Fuzzy Systems, 16(5), pp. 1161-1179
[edytuj]
Wybrane publikacje w dostępie otwartym (open access)
- P. Klęsk (2024), FastRealBoostBins: An ensemble classifier for fast predictions implemented in Python via numba.jit and numba.cuda, SoftwareX, Elsevier, DOI: 10.1016/j.softx.2024.101644
- P. Klęsk (2024), Understanding the Flows of Signals and Gradients: A Tutorial on Algorithms Needed to Implement a Deep Neural Network from Scratch, Applied Sciences 14(21), 9972, Special Issue Advanced Digital Signal Processing and Its Applications
- P. Klęsk, M. Korzeń (2020), Can Boosted Randomness Mimic Learning Algorithms of Geometric Nature? Example of a Simple Algorithm That Converges in Probability to Hard-Margin SVM, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3059653
- D. Sychel, P. Klęsk, A. Bera (2020), Relaxed Per-Stage Requirements for Training Cascades of Classifiers, in 'European Conference on Artificial Intelligence, ECAI 2020', IOS Press, pp. 1523-1530
- P. Klęsk (2017), Constant-Time Fourier Moments for Face Detection - Can Accuracy of Haar-Like Features Be Beaten?, in 'International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, Zakopane, Poland', Lecture Notes in Computer Science book series (LNCS, volume 10245), Springer International Publishing, pp. 530-543
[edytuj]
Tutorial o głębokich sieciach neuronowych
[edytuj]
Zajęcia prowadzone w języku polskim
- Algorytmy 2
- Sztuczna inteligencja
- Uczenie maszynowe 2
- Modelowanie i analiza sekwencji DNA
- Algorytmy, NP-zupełność, redukcje
[edytuj]
