| Processing of missing data by neural networks M Smieja, Ł Struski, J Tabor, B Zieliński, P Spurek arXiv preprint arXiv:1805.07405, 2018 | 37 | 2018 |
| Generalized RBF kernel for incomplete data M Śmieja, Ł Struski, J Tabor, M Marzec Knowledge-Based Systems 173, 150-162, 2019 | 15 | 2019 |
| Generalized RBF kernel for incomplete data M Śmieja, Ł Struski, J Tabor, M Marzec Knowledge-Based Systems 173, 150-162, 2019 | 15 | 2019 |
| Lossy compression approach to subspace clustering Ł Struski, J Tabor, P Spurek Information Sciences 435, 161-183, 2018 | 9 | 2018 |
| Geometric graph convolutional neural networks P Spurek, T Danel, J Tabor, M Smieja, L Struski, A Slowik, L Maziarka arXiv preprint arXiv:1909.05310, 2019 | 7* | 2019 |
| A classification-based approach to semi-supervised clustering with pairwise constraints M Śmieja, Ł Struski, MAT Figueiredo Neural Networks 127, 193-203, 2020 | 5 | 2020 |
| Semi-supervised model-based clustering with controlled clusters leakage M Śmieja, Ł Struski, J Tabor Expert Systems with Applications 85, 146-157, 2017 | 5 | 2017 |
| Can autoencoders help with filling missing data M Śmieja, M Kołomycki, L Struski, M Juda, MAT Figueiredo ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 4 | 2020 |
| Neuro-SERKET: development of integrative cognitive system through the composition of deep probabilistic generative models T Taniguchi, T Nakamura, M Suzuki, R Kuniyasu, K Hayashi, A Taniguchi, ... New Generation Computing, 1-26, 2020 | 3* | 2020 |
| Semi-supervised model-based clustering with controlled clusters leakage. M Smieja, L Struski, J Tabor Expert Syst. Appl. 85, 146-157, 2017 | 3 | 2017 |
| Cone-fields without constant orbit core dimension Ł Struski, J Tabor, T Kułaga Discrete & Continuous Dynamical Systems-A 32 (10), 3651, 2012 | 3 | 2012 |
| Incomplete data representation for SVM classification L Struski, M Smieja, J Tabor arXiv preprint arXiv:1612.01480, 2016 | 2 | 2016 |
| Expansivity and cone-fields in metric spaces Ł Struski, J Tabor Journal of Dynamics and Differential Equations 26 (3), 517-527, 2014 | 2 | 2014 |
| Iterative imputation of missing data using auto-encoder dynamics M Śmieja, M Kołomycki, Ł Struski, M Juda, MAT Figueiredo International Conference on Neural Information Processing, 258-269, 2020 | 1 | 2020 |
| Processing of incomplete images by (graph) convolutional neural networks T Danel, M Śmieja, Ł Struski, P Spurek, Ł Maziarka International Conference on Neural Information Processing, 512-523, 2020 | 1 | 2020 |
| Deep processing of structured data Ł Maziarka, M Śmieja, A Nowak, J Tabor, Ł Struski, P Spurek | 1 | 2018 |
| Subspace memory clustering Ł Struski, J Tabor, P Spurek Schedae Informaticae 24, 2015 | 1 | 2015 |
| HyperPocket: Generative Point Cloud Completion P Spurek, A Kasymov, M Mazur, D Janik, S Tadeja, Ł Struski, J Tabor, ... arXiv preprint arXiv:2102.05973, 2021 | | 2021 |
| ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery D Rymarczyk, Ł Struski, J Tabor, B Zieliński arXiv preprint arXiv:2011.14340, 2020 | | 2020 |
| Estimating conditional density of missing values using deep gaussian mixture model M Przewięźlikowski, M Śmieja, Ł Struski International Conference on Neural Information Processing, 220-231, 2020 | | 2020 |
