| 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 | 468 | 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 | 201 | 2014 |
| Classification restricted Boltzmann machine for comprehensible credit scoring model JM Tomczak, M Zięba Expert Systems with Applications 42 (4), 1789-1796, 2015 | 110 | 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 | 106 | 2020 |
| Diffused heads: Diffusion models beat gans on talking-face generation M Stypułkowski, K Vougioukas, S He, M Zięba, S Petridis, M Pantic Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 90 | 2024 |
| Bingan: Learning compact binary descriptors with a regularized gan M Zieba, P Semberecki, T El-Gaaly, T Trzcinski NeurIPS 2018, 2018 | 86 | 2018 |
| UCSG-Net--Unsupervised Discovering of Constructive Solid Geometry Tree K Kania, M Zięba, T Kajdanowicz NeurIPS 2020, 2020 | 85 | 2020 |
| 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 | 70 | 2018 |
| Boosted SVM with active learning strategy for imbalanced data M Zięba, JM Tomczak Soft Computing 19 (12), 3357-3368, 2015 | 49 | 2015 |
| Hypernetwork approach to generating point clouds P Spurek, S Winczowski, J Tabor, M Zamorski, M Zięba, T Trzciński ICML 2020, 2020 | 38 | 2020 |
| 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 | 32 | 2014 |
| Probabilistic combination of classification rules and its application to medical diagnosis JM Tomczak, M Zięba Machine Learning 101, 105-135, 2015 | 25 | 2015 |
| Hypershot: Few-shot learning by kernel hypernetworks M Sendera, M Przewięźlikowski, K Karanowski, M Zięba, J Tabor, ... WACV 2023, 2022 | 22 | 2022 |
| Adversarial autoencoders for generating 3d point clouds M Zamorski, M Zieba, R Nowak, W Stokowiec, T Trzcinski arXiv preprint arXiv:1811.07605 2 (3), 2018 | 22 | 2018 |
| Training triplet networks with gan M Zieba, L Wang ICLR 2017 Workshop track, 2017 | 22 | 2017 |
| Speech driven video editing via an audio-conditioned diffusion model D Bigioi, S Basak, M Stypułkowski, M Zieba, H Jordan, R McDonnell, ... Image and Vision Computing 142, 104911, 2024 | 21 | 2024 |
| Generative adversarial networks: recent developments M Zamorski, A Zdobylak, M Zięba, J Świątek Artificial Intelligence and Soft Computing: 18th International Conference …, 2019 | 20 | 2019 |
| Non-Gaussian Gaussian Processes for Few-Shot Regression M Sendera, J Tabor, A Nowak, A Bedychaj, M Patacchiola, T Trzciński, ... NeurIPS 2021, 2021 | 18 | 2021 |
| RBM-SMOTE: restricted boltzmann machines for synthetic minority oversampling technique M Zięba, JM Tomczak, A Gonczarek Intelligent Information and Database Systems: 7th Asian Conference, ACIIDS …, 2015 | 18 | 2015 |
| The proposal of Service Oriented Data Mining System for solving real-life classification and regression problems A Prusiewicz, M Zięba Technological Innovation for Sustainability: Second IFIP WG 5.5/SOCOLNET …, 2011 | 16 | 2011 |
Jakub M. TomczakAssociate professor at Eindhoven University of Technology在 tue.nl 的电子邮件经过验证