| Lifted relational neural networks: Efficient learning of latent relational structures G Sourek, V Aschenbrenner, F Zelezny, S Schockaert, O Kuzelka Journal of Artificial Intelligence Research 62, 69-100, 2018 | 157 | 2018 |
| Neural markov logic networks G Marra, O Kuželka Uncertainty in Artificial Intelligence, 908-917, 2021 | 63 | 2021 |
| Block-wise construction of tree-like relational features with monotone reducibility and redundancy O Kuželka, F Železný Machine Learning 83, 163-192, 2011 | 47 | 2011 |
| A restarted strategy for efficient subsumption testing O Kuželka, F Železný Fundamenta Informaticae 89 (1), 95-109, 2008 | 37 | 2008 |
| Weighted first-order model counting in the two-variable fragment with counting quantifiers O Kuzelka Journal of Artificial Intelligence Research 70, 1281-1307, 2021 | 30 | 2021 |
| Lifted relational neural networks G Šourek, V Aschenbrenner, F Zelezný, O Kuzelka Proceedings of the NIPS Workshop on Cognitive Computation: Integrating …, 2015 | 30 | 2015 |
| Relational marginal problems: Theory and estimation O Kuželka, Y Wang, J Davis, S Schockaert Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 24 | 2018 |
| Hifi: Tractable propositionalization through hierarchical feature construction O Kuželka, F Železný Late breaking papers, the 18th international conference on inductive logic …, 2008 | 24 | 2008 |
| Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search A Szabóová, O Kuželka, F Železný, J Tolar BMC bioinformatics 13, 1-11, 2012 | 22 | 2012 |
| Lifted inference with tree axioms T Van Bremen, O Kuželka Artificial Intelligence, 103997, 2023 | 20 | 2023 |
| Encoding Markov logic networks in possibilistic logic O Kuzelka, J Davis, S Schockaert arXiv preprint arXiv:1506.01432, 2015 | 20 | 2015 |
| Lossless compression of structured convolutional models via lifting G Sourek, F Zelezny, O Kuzelka arXiv preprint arXiv:2007.06567, 2020 | 19 | 2020 |
| Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption O Kuželka, J Davis Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial …, 2019 | 19 | 2019 |
| Learning predictive categories using lifted relational neural networks G Šourek, S Manandhar, F Železný, S Schockaert, O Kuželka Inductive Logic Programming: 26th International Conference, ILP 2016, London …, 2017 | 17 | 2017 |
| Faster lifting for two-variable logic using cell graphs T van Bremen, O Kuželka Uncertainty in Artificial Intelligence, 1393-1402, 2021 | 16 | 2021 |
| Beyond graph neural networks with lifted relational neural networks G Šourek, F Železný, O Kuželka Machine Learning 110 (7), 1695-1738, 2021 | 16 | 2021 |
| Induction of interpretable possibilistic logic theories from relational data O Kuzelka, J Davis, S Schockaert arXiv preprint arXiv:1705.07095, 2017 | 16 | 2017 |
| Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties O Kuželka, F Železný Proceedings of the 26th annual international conference on machine learning …, 2009 | 16 | 2009 |
| Fast estimation of first-order clause coverage through randomization and maximum likelihood O Kuželka, F Železný Proceedings of the 25th international conference on Machine learning, 504-511, 2008 | 15 | 2008 |
| Stacked structure learning for lifted relational neural networks G Šourek, M Svatoš, F Železný, S Schockaert, O Kuželka Inductive Logic Programming: 27th International Conference, ILP 2017 …, 2018 | 14 | 2018 |
Filip ZeleznyCzech Technical University in PragueVerified email at fel.cvut.cz
