Email: andrzej.rusiecki@pwr.edu.pl

Unit: Faculty of Information and Communication Technology (N) » Department of Computer Engineering

ul. Z. Janiszewskiego 11/17, 50-372 Wrocław
building C-3, room 230
phone +48 71 320 4226

Research fields

• Deep learning; artificial neural networks; learning algorithms; image processing; robust statistics.

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Recent papers

2016

• Kordos M., Rusiecki A., Reducing noise impact on MLP training. Soft Computing. 2016, vol. 20, nr 1, s. 49-65.
• Halawa K., Bazan M., Ciskowski P., Janiczek T., Kozaczewski P., Rusiecki A., Road traffic predictions across major city intersections using multilayer perceptrons and data from multiple intersections located in various places. IET Intelligent Transport Systems. 2016, vol. 10, nr 7, s. 469-475.

2015

• Rusiecki A., Kordos M., Effectiveness of unsupervised training in deep learning neural networks. Schedae Informaticae. 2015, vol. 24, s. 1-10.
• Bazan M., Bożek M., Ciskowski P., Halawa K., Janiczek T., Kozaczewski P., Rusiecki A., Some acpects of Intelligent Transport System auditing. Archives of Transport System Telematics. 2015, vol. 8, nr 3, s. 3-8.

2014

• Rusiecki A., Kordos M., Kamiński T., Greń K., Training neural networks on noisy data. W: Artificial intelligence and soft computing: 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014: proceedings. Pt. 1 / Leszek Rutkowski [i in.] (eds.). Cham [i in.]: Springer, cop. 2014. s. 131-142 (Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, ISSN 0302-9743; vol. 8467).

2013

• Rusiecki A., Robust learning algorithm based on LTA estimator. Neurocomputing. 2013, vol. 120, s. 624-632.

2012

• Rusiecki A., Robust learning algorithm based on iterative least median of squares. Neural Processing Letters. 2012, vol. 36, nr 2, s. 145-160.

Papers in DONA database

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1	Proceeding paper 2022 Andrzej Rusiecki, Batch normalization and dropout regularization in training deep neural networks with label noise. W: Intelligent Systems Design and Applications : 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021), December 13-15, 2021 / eds. Ajith Abraham [i in.]. Cham : Springer, cop. 2022. s. 57-66. ISBN: 978-3-030-96307-1; 978-3-030-96308-8 Resources:DOISFX
2	Proceeding paper 2022 Agnieszka Kazimierska, Cyprian Mataczyński, Agnieszka Uryga, Małgorzata Burzyńska, Andrzej Rusiecki, Magdalena Kasprowicz, Analysis of the relationship between intracranial pressure pulse waveform and outcome in traumatic brain injury. W: Innovations and developments of technologies in medicine, biology and healthcare : proceedings of the IEEE EMBS International Student Conference (ISC) / eds. Natalia Piaseczna, Magdalena Gorczowska, Agnieszka Łach. Cham : Springer, cop. 2022. s. 52-57. ISBN: 978-3-030-88975-3; 978-3-030-88976-0 Resources:DOIURLSFX
3	Article 2022 Cyprian Mataczyński, Agnieszka Kazimierska, Agnieszka Uryga, Małgorzata Burzyńska, Andrzej Rusiecki, Magdalena Kasprowicz, End-to-end automatic morphological classification of intracranial pressure pulse waveforms using deep learning. IEEE Journal of Biomedical and Health Informatics. 2022, vol. 26, nr 2, s. 494-504. ISSN: 2168-2194; 2168-2208 Resources:DOIURLSFX
4	Proceeding paper 2021 Agnieszka Kazimierska, Agnieszka Uryga, Cyprian Mataczyński, Małgorzata Burzyńska, Arkadiusz Ziółkowski, Andrzej Rusiecki, Magdalena Kasprowicz, Analysis of the shape of intracranial pressure pulse waveform in traumatic brain injury patients. W: 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, October 30 - November 5, 2021, Virtual Conference. [Danvers, MA : IEEE, cop. 2021]. s. 546-549. ISBN: 978-1-7281-1179-7; 978-1-7281-1178-0 Resources:DOIURL
5	Proceeding paper 2021 Michał Zendran, Andrzej Rusiecki, Swapping face images with generative neural networks for Deepfake technology – experimental study. W: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES 2021 / eds. Jaroslaw Watrobski [i in.]. Amsterdam : Elsevier, cop. 2021. s. 834-843. Resources:DOISFX
6	Proceeding paper 2020 Andrzej Rusiecki, Standard dropout as remedy for training deep neural networks with label noise. W: Theory and Applications of Dependable Computer Systems : proceedings of the Fifteenth International Conference on Dependability of Computer Systems DepCoS-RELCOMEX, June 29 - July 3, 2020, Brunów, Poland / eds. Wojciech Zamojski [i in.]. Cham : Springer, cop. 2020. s. 534-542. ISBN: 978-3-030-48255-8; 978-3-030-48256-5 Resources:DOIURLSFX
7	Proceeding paper 2019 Andrzej Rusiecki, Trimmed robust loss function for training deep neural networks with label noise. W: Artificial Intelligence and Soft Computing : 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16-20, 2019 : proceedings. Pt. 1 / eds. Leszek Rutkowski [i in.]. Switzerland : Springer, cop. 2019. s. 215-222. ISBN: 978-3-030-20911-7 Resources:DOIURLSFX
8	Article 2019 Andrzej Rusiecki, Trimmed categorical cross-entropy for deep learning with label noise. Electronics Letters. 2019, vol. 55, nr 6, s. 319-320. ISSN: 0013-5194 Resources:DOISFX
9	Proceeding paper 2018 Bartosz Miselis, Michał Kulus, Tomasz Jurek, Andrzej Rusiecki, Łukasz Jeleń, Deep neural network for whole slide vein segmentation. W: Computer Information Systems and Industrial Management : 17th International Conference, CISIM 2018, Olomouc, Czech Republic, September 27-29, 2018 : proceedings / eds. Khalid Saeed, Władysław Homenda. [Cham] : Springer, cop. 2018. s. 57–67. ISBN: 978-3-319-99953-1 Resources:DOISFX
10	Article 2016 Marek Bazan, Piotr Ciskowski, Krzysztof Halawa, Tomasz Janiczek, Łukasz Madej, Andrzej Rusiecki, Green wave optimisation. Archives of Transport System Telematics. 2016, vol. 9, nr 3, s. 3-8. ISSN: 1899-8208 Resources:URLSFX