| 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 | 89 | 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 | 35* | 2020 |
| A classification-based approach to semi-supervised clustering with pairwise constraints M Śmieja, Ł Struski, MAT Figueiredo Neural Networks 127, 193-203, 2020 | 26 | 2020 |
| Generalized RBF kernel for incomplete data M Śmieja, Ł Struski, J Tabor, M Marzec Knowledge-Based Systems 173, 150-162, 2019 | 26 | 2019 |
| Generalized RBF kernel for incomplete data M Śmieja, Ł Struski, J Tabor, M Marzec Knowledge-Based Systems 173, 150-162, 2019 | 26 | 2019 |
| Lossy compression approach to subspace clustering Ł Struski, J Tabor, P Spurek Information Sciences 435, 161-183, 2018 | 17 | 2018 |
| Protopshare: Prototypical parts sharing for similarity discovery in interpretable image classification D Rymarczyk, Ł Struski, J Tabor, B Zieliński Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 8 | 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 | 8 | 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 | 7 | 2020 |
| Semi-supervised model-based clustering with controlled clusters leakage M Śmieja, Ł Struski, J Tabor Expert Systems with Applications 85, 146-157, 2017 | 7 | 2017 |
| 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 | 4 | 2021 |
| 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 | 4 | 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 | 4 | 2020 |
| Can auto-encoders help with filling missing data? M Śmieja, M Kołomycki, Ł Struski, M Juda, MAT Figueiredo ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 4 | 2020 |
| Cone-fields without constant orbit core dimension Ł Struski, J Tabor, T Kułaga Discrete & Continuous Dynamical Systems 32 (10), 3651, 2012 | 4 | 2012 |
| 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 | 3 | 2021 |
| Flow-based anomaly detection Ł Maziarka, M Śmieja, M Sendera, Ł Struski, J Tabor, P Spurek arXiv preprint arXiv:2010.03002, 2020 | 3* | 2020 |
| Interpretable image classification with differentiable prototypes assignment D Rymarczyk, Ł Struski, M Górszczak, K Lewandowska, J Tabor, ... arXiv preprint arXiv:2112.02902, 2021 | 2 | 2021 |
| 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 |
Jacek TaborProfesor informatyki, Uniwersytet JagiellońskiZweryfikowany adres z ii.uj.edu.pl
