| Processing of missing data by neural networks M Śmieja, Ł Struski, J Tabor, B Zieliński, P Spurek Advances in neural information processing systems 31, 2018 | 153 | 2018 |
| Spatial graph convolutional networks T Danel, P Spurek, J Tabor, M Śmieja, Ł Struski, A Słowik, Ł Maziarka International Conference on Neural Information Processing, 668-675, 2020 | 99* | 2020 |
| Hypernetwork functional image representation S Klocek, Ł Maziarka, M Wołczyk, J Tabor, J Nowak, M Śmieja International Conference on Artificial Neural Networks, 496-510, 2019 | 82 | 2019 |
| A classification-based approach to semi-supervised clustering with pairwise constraints M Śmieja, Ł Struski, MAT Figueiredo Neural Networks 127, 193-203, 2020 | 40 | 2020 |
| Zero time waste: Recycling predictions in early exit neural networks M Wołczyk, B Wójcik, K Bałazy, IT Podolak, J Tabor, M Śmieja, T Trzcinski Advances in Neural Information Processing Systems 34, 2516-2528, 2021 | 37 | 2021 |
| Generalized RBF kernel for incomplete data M Śmieja, Ł Struski, J Tabor, M Marzec Knowledge-Based Systems 173, 150-162, 2019 | 35 | 2019 |
| Constrained clustering with a complex cluster structure M Śmieja, M Wiercioch Advances in Data Analysis and Classification 11, 493-518, 2017 | 27 | 2017 |
| Segma: Semi-supervised gaussian mixture autoencoder M Śmieja, M Wołczyk, J Tabor, BC Geiger IEEE transactions on neural networks and learning systems 32 (9), 3930-3941, 2021 | 22 | 2021 |
| Semi-supervised cross-entropy clustering with information bottleneck constraint M Śmieja, BC Geiger Information Sciences 421, 254-271, 2017 | 20 | 2017 |
| Entropy of the Mixture of Sources and Entropy Dimension M Smieja, J Tabor Information Theory, IEEE Transactions on, 1-1, 2011 | 20 | 2011 |
| Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design D Warszycki, Ł Struski, M Smieja, R Kafel, R Kurczab Journal of chemical information and modeling 61 (10), 5054-5065, 2021 | 18 | 2021 |
| Semi-supervised discriminative clustering with graph regularization M Śmieja, O Myronov, J Tabor Knowledge-Based Systems 151, 24-36, 2018 | 18 | 2018 |
| SVM with a neutral class M Śmieja, J Tabor, P Spurek Pattern Analysis and Applications 22, 573-582, 2019 | 17 | 2019 |
| Average Information Content Maximization—A New Approach for Fingerprint Hybridization and Reduction M Śmieja, D Warszycki PLoS ONE 11 (1), e0146666, 2016 | 17 | 2016 |
| Weighted approach to general entropy function M Śmieja IMA Journal of Mathematical Control and Information 32 (2), 329-341, 2015 | 16 | 2015 |
| Efficient mixture model for clustering of sparse high dimensional binary data M Śmieja, K Hajto, J Tabor Data Mining and Knowledge Discovery 33, 1583-1624, 2019 | 15 | 2019 |
| Fast independent component analysis algorithm with a simple closed-form solution P Spurek, J Tabor, M Śmieja Knowledge-Based Systems 161, 26-34, 2018 | 15 | 2018 |
| Asymmetric Clustering Index in a Case Study of 5-HT1A Receptor Ligands M Śmieja, D Warszycki, J Tabor, AJ Bojarski PloS one 9 (7), e102069, 2014 | 14 | 2014 |
| Adversarial examples detection and analysis with layer-wise autoencoders B Wójcik, P Morawiecki, M Śmieja, T Krzyżek, P Spurek, J Tabor 2021 IEEE 33rd International Conference on Tools with Artificial …, 2021 | 13 | 2021 |
| Estimating conditional density of missing values using deep gaussian mixture model M Przewięźlikowski, M Śmieja, Ł Struski Neural Information Processing: 27th International Conference, ICONIP 2020 …, 2020 | 11 | 2020 |
Jacek TaborProfesor informatyki, Uniwersytet Jagielloński在 ii.uj.edu.pl 的电子邮件经过验证
