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Contact:
Room: 502

Position:
Assistant Professor

Research teams:
Struktury i metody uczenia sieci neuronowych

PhD Eng Jacek Smoląg

Papers (18)

2023 (3)

On Speeding up the Levenberg-Marquardt Learning Algorithm
Bilski J., Kowalczyk B., Smolag J., On Speeding up the Levenberg-Marquardt Learning Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 12-22, 2023, Cites: 0
Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks
Bilski J., Smolag J., Kowalczyk B., Grzanek K., Izonin I., Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks, Journal of Artificial Intelligence and Soft Computing Research, 13, 13, 45-61, 2023, Cites: 12
A New Computational Approach to the Levenberg-Marquardt Learning Algorithm
Bilski J., Kowalczyk B., Smolag J., A New Computational Approach to the Levenberg-Marquardt Learning Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13588 LNAI, 13588 LNAI, 16-26, 2023, Cites: 0

2021 (2)

Modification of Learning Feedforward Neural Networks with the BP Method
Bilski J., Smolag J., Najgebauer P., Modification of Learning Feedforward Neural Networks with the BP Method, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 54-65, 2021, Cites: 2
A novel method for speed training acceleration of recurrent neural networks
Bilski J., Rutkowski L., Smolag J., Tao D., A novel method for speed training acceleration of recurrent neural networks, Information Sciences, 553, 553, 266-279, 2021, Cites: 19

2020 (1)

Fast Conjugate Gradient Algorithm for Feedforward Neural Networks
Bilski J., Smolag J., Fast Conjugate Gradient Algorithm for Feedforward Neural Networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 27-38, 2020, Cites: 4

2017 (1)

Parallel realizations of the iterative statistical reconstruction algorithm for 3D computed tomography
Cierniak R., Bilski J., Smolag J., Pluta P., Shah N., Parallel realizations of the iterative statistical reconstruction algorithm for 3D computed tomography, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 473-484, 2017, Cites: 1

2016 (1)

Predicting success of bank direct marketing by neuro-fuzzy systems
Scherer M., Smolag J., Gaweda A., Predicting success of bank direct marketing by neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 570-576, 2016, Cites: 3

2015 (2)

Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks
Bilski J., Smolag J., Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks, IEEE Transactions on Parallel and Distributed Systems, 26, 26, 2561-2570, 2015, Cites: 39
Parallel approach to the Levenberg-marquardt learning algorithm for feedforward neural networks
Bilski J., Smolag J., Zurada J.M., Parallel approach to the Levenberg-marquardt learning algorithm for feedforward neural networks, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 3-14, 2015, Cites: 22

2014 (1)

The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks
Bilski J., Smolag J., Galushkin A.I., The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 12-21, 2014, Cites: 26

2013 (1)

Parallel approach to learning of the recurrent Jordan neural network
Bilski J., Smolag J., Parallel approach to learning of the recurrent Jordan neural network, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 32-40, 2013, Cites: 26

2012 (1)

Parallel realisation of the recurrent multi layer perceptron learning
Bilski J., Smolag J., Parallel realisation of the recurrent multi layer perceptron learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 12-20, 2012, Cites: 24

2010 (2)

Parallel realisation of the recurrent Elman neural network learning
Bilski J., Smolag J., Parallel realisation of the recurrent Elman neural network learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 19-25, 2010, Cites: 24
Distributed control system based on real time ethernet for computer numerical controlled machine tool
PrzybyL A., Smolag J., Kimla P., Distributed control system based on real time ethernet for computer numerical controlled machine tool, Przeglad Elektrotechniczny, 86, 86, 342-346, 2010, Cites: 16

2008 (1)

Parallel realisation of the recurrent RTRN neural network learning
Bilski J., Smolag J., Parallel realisation of the recurrent RTRN neural network learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 11-16, 2008, Cites: 27

2004 (2)

Parallel realisation of QR algorithm for neural networks learning
Bilski J., Litwinski S., Smolag J., Parallel realisation of QR algorithm for neural networks learning, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 158-165, 2004, Cites: 20
Systolic architectures for soft computing algorithms
Bilski J., Smolag J., Zurada J., Systolic architectures for soft computing algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 601-608, 2004, Cites: 0

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