| Comparison of GPU and FPGA implementation of SVM algorithm for fast image segmentation M Pietron, M Wielgosz, D Zurek, E Jamro, K Wiatr Architecture of Computing Systems–ARCS 2013: 26th International Conference …, 2013 | 27 | 2013 |
| Ensemble neuroevolution-based approach for multivariate time series anomaly detection K Faber, M Pietron, D Zurek Entropy 23 (11), 1466, 2021 | 22 | 2021 |
| The comparison of parallel sorting algorithms implemented on different hardware platforms D Żurek, M Pietroń, M Wielgosz, K Wiatr Computer Science 14 (4)), 679--691, 2013 | 22 | 2013 |
| Toward hybrid platform for evolutionary computations of hard discrete problems D Żurek, K Piętak, M Pietroń, M Kisiel-Dorohinicki Procedia Computer Science 108, 877-886, 2017 | 13 | 2017 |
| FPGA implementation of the selected parts of the fast image segmentation M Wielgosz, E Jamro, D Żurek, K Wiatr Intelligent Tools for Building a Scientific Information Platform, 203-216, 2012 | 13 | 2012 |
| Striving for performance of discrete optimisation via memetic agent-based systems in a hybrid CPU/GPU environment K Piętak, D Żurek, M Pietroń, A Dymara, M Kisiel-Dorohinicki Journal of Computational Science 31, 151-162, 2019 | 7 | 2019 |
| Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction M Pietroń, D Żurek, B Śnieżyński Journal of Computational Science 67, 101971, 2023 | 6 | 2023 |
| Evaluation and implementation of n-Gram-Based algorithm for fast text comparison M Wielgosz, P Russek, E Jamro, K Wiatr Computing and Informatics 36 (4), 887-907, 2017 | 5 | 2017 |
| Implementation of algorithms for fast text search and files comparison E Jamro, M Wielgosz, P Russek, M Pietroń, D Żurek, M Janiszewski, ... Proceedings the High Performance Computer Users Conference KU KDM, 83-84, 2013 | 3 | 2013 |
| AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection M Pietron, D Zurek, K Faber, R Corizzo arXiv preprint arXiv:2305.16497, 2023 | 2 | 2023 |
| New variants of sdls algorithm for labs problem dedicated to gpgpu architectures D Żurek, K Piętak, M Pietroń, M Kisiel-Dorohinicki International Conference on Computational Science, 206-212, 2021 | 2 | 2021 |
| From mnist to imagenet and back: Benchmarking continual curriculum learning K Faber, D Zurek, M Pietron, N Japkowicz, A Vergari, R Corizzo Machine Learning, 1-28, 2024 | 1 | 2024 |
| Accelerating Deep Convolutional Neural on GPGPU D Żurek, M Pietroń, K Wiatr Intelligent Computing: Proceedings of the 2021 Computing Conference, Volume …, 2021 | 1 | 2021 |
| When deep learning models on GPU can be accelerated by taking advantage of unstructured sparsity M Pietroń, D Żurek arXiv preprint arXiv:2011.06295, 2020 | 1 | 2020 |
| AD-NEv++: The multi-architecture neuroevolution-based multivariate anomaly detection framework M Pietroń, D Żurek, K Faber, R Corizzo arXiv preprint arXiv:2404.07968, 2024 | | 2024 |
| Towards efficient deep autoencoders for multivariate time series anomaly detection M Pietroń, D Żurek, K Faber, R Corizzo arXiv preprint arXiv:2403.02429, 2024 | | 2024 |
| Ada-QPacknet--adaptive pruning with bit width reduction as an efficient continual learning method without forgetting M Pietroń, D Żurek, K Faber, R Corizzo arXiv preprint arXiv:2308.07939, 2023 | | 2023 |
| Transformed-*: A domain-incremental lifelong learning scenario generation framework D Zurek, R Corizzo, M Karwatowski, M Pietron, K Faber 2023 International Joint Conference on Neural Networks (IJCNN), 1-10, 2023 | | 2023 |
| A Deep Neural Network as a TABU Support in Solving LABS Problem D Żurek, M Pietroń, K Piętak, M Kisiel-Dorohinicki International Conference on Computational Science, 237-243, 2022 | | 2022 |
| FAST AND SCALABLE NEUROEVOLUTION DEEP LEARNING ARCHITECTURE SEARCH FOR MULTIVARIATE ANOMALY DETECTION D Zurek, K Faber arXiv preprint arXiv:2112.05640, 2021 | | 2021 |
Maciej WielgoszAGH University of Science and TechnologyVerified email at agh.edu.pl
