FPGA Implementation of the Selected Parts of the Fast Image Segmentation. - Maciej Wielgosz; Ernest Jamro; Dominik Zurek; Kazimierz Wiatr (Intelligent Tools for Building a Scientific Information Platform, 2012)

Comparison of GPU and FPGA Implementation of SVM Algorithm for Fast Image Segmentation. - Marcin Pietron; Maciej Wielgosz; Dominik Zurek; Ernest Jamro; Kazimierz Wiatr (ARCS, 2013)

Comparison of Hybrid Sorting Algorithms Implemented on Different Parallel Hardware Platforms. - Dominik Zurek; Marcin Pietron; Maciej Wielgosz; Kazimierz Wiatr (Comput. Sci., 2013)

Modifications of most expressive feature reordering criteria for supervised kernel Principal Component Analysis. - Krzysztof Adamiak; Piotr Duch; Dominik Zurek; Krzysztof Slot (CYBCONF, 2015)

Liveness detection in remote biometrics based on gaze direction estimation. - Krzysztof Adamiak; Dominik Zurek; Krzysztof Slot (FedCSIS, 2015)

Toward hybrid platform for evolutionary computations of hard discrete problems. - Dominik Zurek; Kamil Pietak; Marcin Pietron; Marek Kisiel-Dorohinicki (ICCS, 2017)

Evaluation and Implementation of n-Gram-Based Algorithm for Fast Text Comparison. - Maciej Wielgosz; Pawel Szczepka; Pawel Russek; Ernest Jamro; Kazimierz Wiatr; Marcin Pietron; Dominik Zurek (Comput. Informatics, 2017)

Striving for performance of discrete optimisation via memetic agent-based systems in a hybrid CPU/GPU environment. - Kamil Pietak; Dominik Zurek; Marcin Pietron; Andrzej Dymara; Marek Kisiel-Dorohinicki (J. Comput. Sci., 2019)

Training with reduced precision of a support vector machine model for text classification. - Dominik Zurek; Marcin Pietron (CoRR, 2020)

When deep learning models on GPU can be accelerated by taking advantage of unstructured sparsity. - Marcin Pietron; Dominik Zurek (CoRR, 2020)

New Variants of SDLS Algorithm for LABS Problem Dedicated to GPGPU Architectures. - Dominik Zurek; Kamil Pietak; Marcin Pietron; Marek Kisiel-Dorohinicki (ICCS (1), 2021)

Accelerating Deep Convolutional Neural on GPGPU. - Dominik Zurek; Marcin Pietron; Kazimierz Wiatr (SAI (2), 2021)

Ensemble neuroevolution based approach for multivariate time series anomaly detection. - Kamil Faber; Dominik Zurek; Marcin Pietron; Kamil Pietak (CoRR, 2021)

Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection. - Marcin Pietron; Dominik Zurek; Kamil Faber (CoRR, 2021)

Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction. - Marcin Pietron; Dominik Zurek (CoRR, 2021)

Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection. - Kamil Faber; Marcin Pietron; Dominik Zurek (Entropy, 2021)

A Deep Neural Network as a TABU Support in Solving LABS Problem. - Dominik Zurek; Marcin Pietron; Kamil Pietak; Marek Kisiel-Dorohinicki (ICCS (1), 2022)

Ada-QPacknet - Multi-Task Forget-Free Continual Learning with Quantization Driven Adaptive Pruning. - Marcin Pietron; Dominik Zurek; Kamil Faber; Roberto Corizzo (ECAI, 2023)

Transformed-*: A domain-incremental lifelong learning scenario generation framework. - Dominik Zurek; Roberto Corizzo; Michal Karwatowski; Marcin Pietron; Kamil Faber (IJCNN, 2023)

AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection. - Marcin Pietron; Dominik Zurek; Kamil Faber; Roberto Corizzo (CoRR, 2023)

Ada-QPacknet - adaptive pruning with bit width reduction as an efficient continual learning method without forgetting. - Marcin Pietron; Dominik Zurek; Kamil Faber; Roberto Corizzo (CoRR, 2023)

Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction. - Marcin Pietron; Dominik Zurek; Bartlomiej Sniezynski (J. Comput. Sci., 2023)

RLEM: Deep Reinforcement Learning Ensemble Method for Aircraft Recovery Problem. - Dominik Zurek; Marcin Pietron; Szymon Piórkowski; Michal Karwatowski; Kamil Faber (IEEE Big Data, 2024)

A Deep Double Q-Learning as a SDLS support in solving LABS problem. - Dominik Zurek; Marcin Pietron; Kamil Pietak; Kamil Faber (ESANN, 2024)

AD-NEv++ - The multi-architecture neuroevolution-based multivariate anomaly detection framework. - Marcin Pietron; Dominik Zurek; Kamil Faber; Roberto Corizzo; Anna Wójcik (GECCO Companion, 2024)

Towards Efficient Deep Autoencoders for Multivariate Time Series Anomaly Detection. - Marcin Pietron; Dominik Zurek; Kamil Faber; Roberto Corizzo (ICCS (3), 2024)

AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework. - Marcin Pietron; Dominik Zurek; Kamil Faber; Roberto Corizzo (CoRR, 2024)

Solving Multi-Goal Robotic Tasks with Decision Transformer. - Paul Gajewski; Dominik Zurek; Marcin Pietron; Kamil Faber (CoRR, 2024)

From MNIST to ImageNet and back: benchmarking continual curriculum learning. - Kamil Faber; Dominik Zurek; Marcin Pietron; Nathalie Japkowicz; Antonio Vergari; Roberto Corizzo (Mach. Learn., 2024)

TinySubNets: An Efficient and Low Capacity Continual Learning Strategy. - Marcin Pietron; Kamil Faber; Dominik Zurek; Roberto Corizzo (AAAI, 2025)

AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection. - Marcin Pietron; Dominik Zurek; Kamil Faber; Roberto Corizzo (IEEE Trans. Neural Networks Learn. Syst., 2025)

xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection. - Kamil Faber; Marcin Pietron; Dominik Zurek; Roberto Corizzo (CoRR, 2025)