| Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction M Zięba, SK Tomczak, JM Tomczak Expert Systems with Applications 58, 93-101, 2016 | 216 | 2016 |
| Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients M Zięba, JM Tomczak, M Lubicz, J Świątek Applied soft computing 14, 99-108, 2014 | 127 | 2014 |
| Classification restricted Boltzmann machine for comprehensible credit scoring model JM Tomczak, M Zięba Expert Systems with Applications 42 (4), 1789-1796, 2015 | 64 | 2015 |
| Adversarial autoencoders for compact representations of 3D point clouds M Zamorski, M Zięba, P Klukowski, R Nowak, K Kurach, W Stokowiec, ... Computer Vision and Image Understanding 193, 102921, 2020 | 36 | 2020 |
| Boosted SVM with active learning strategy for imbalanced data M Zięba, JM Tomczak Soft Computing 19 (12), 3357-3368, 2015 | 28 | 2015 |
| Probabilistic combination of classification rules and its application to medical diagnosis JM Tomczak, M Zięba Machine Learning 101 (1), 105-135, 2015 | 24 | 2015 |
| Bingan: Learning compact binary descriptors with a regularized gan M Zieba, P Semberecki, T El-Gaaly, T Trzcinski NeurIPS 2018, 2018 | 23 | 2018 |
| Service-oriented medical system for supporting decisions with missing and imbalanced data M Zięba IEEE journal of biomedical and health informatics 18 (5), 1533-1540, 2014 | 22 | 2014 |
| NMRNet: a deep learning approach to automated peak picking of protein NMR spectra P Klukowski, M Augoff, M Zięba, M Drwal, A Gonczarek, MJ Walczak Bioinformatics 34 (15), 2590-2597, 2018 | 19 | 2018 |
| Ensemble classifier for solving credit scoring problems M Zięba, J Świątek Doctoral Conference on Computing, Electrical and Industrial Systems, 59-66, 2012 | 14 | 2012 |
| The proposal of Service Oriented Data Mining System for solving real-life classification and regression problems A Prusiewicz, M Zięba Doctoral Conference on Computing, Electrical and Industrial Systems, 83-90, 2011 | 14 | 2011 |
| Training triplet networks with gan M Zieba, L Wang ICLR 2017 Workshop track, 2017 | 13 | 2017 |
| RBM-SMOTE: restricted Boltzmann machines for synthetic minority oversampling technique M Zięba, JM Tomczak, A Gonczarek Asian Conference on Intelligent Information and Database Systems, 377-386, 2015 | 12 | 2015 |
| Services Recommendation in Systems Based on Service Oriented Architecture by Applying Modified ROCK Algorithm A Prusiewicz, M Zieba Networked Digital Technologies - Second International Conference, NDT 2010 …, 2010 | 11 | 2010 |
| Generative adversarial networks: recent developments M Zamorski, A Zdobylak, M Zięba, J Świątek International Conference on Artificial Intelligence and Soft Computing, 248-258, 2019 | 10 | 2019 |
| Conditional invertible flow for point cloud generation M Stypułkowski, M Zamorski, M Zięba, J Chorowski NeurIPS 2019 Workshop: Set & Partitions, 2019 | 7 | 2019 |
| Hypernetwork approach to generating point clouds P Spurek, S Winczowski, J Tabor, M Zamorski, M Zięba, T Trzciński ICML 2020, 2020 | 6 | 2020 |
| Analysis of human arm motions recognition algorithms for system to visualize virtual arm K Brzostowski, M Zieba 2011 21st International Conference on Systems Engineering, 422-426, 2011 | 5 | 2011 |
| UCSG-Net--Unsupervised Discovering of Constructive Solid Geometry Tree K Kania, M Zięba, T Kajdanowicz NeurIPS 2020, 2020 | 4 | 2020 |
| Beta-boosted ensemble for big credit scoring data M Zieba, WK Härdle Handbook of Big Data Analytics, 523-538, 2018 | 4 | 2018 |
Jakub M. TomczakAssistant Professor at Vrije Universiteit AmsterdamVerified email at vu.nl
