| LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines R Langone, C Alzate, B De Ketelaere, J Vlasselaer, W Meert, ... Engineering Applications of Artificial Intelligence 37, 268-278, 2015 | 115 | 2015 |
| Problog2: Probabilistic logic programming A Dries, A Kimmig, W Meert, J Renkens, G Van den Broeck, J Vlasselaer, ... Joint european conference on machine learning and knowledge discovery in …, 2015 | 88 | 2015 |
| Anytime inference in probabilistic logic programs with Tp-compilation J Vlasselaer, G Van den Broeck, A Kimmig, W Meert, L De Raedt, Q Yang, ... Proceedings of 24th International Joint Conference on Artificial …, 2015 | 50 | 2015 |
| Tp-compilation for inference in probabilistic logic programs J Vlasselaer, G Van den Broeck, A Kimmig, W Meert, L De Raedt International Journal of Approximate Reasoning 78, 15-32, 2016 | 40 | 2016 |
| Exploiting local and repeated structure in dynamic Bayesian networks J Vlasselaer, W Meert, G Van den Broeck, L De Raedt Artificial Intelligence 232, 43-53, 2016 | 38 | 2016 |
| The most probable explanation for probabilistic logic programs with annotated disjunctions D Shterionov, J Renkens, J Vlasselaer, A Kimmig, W Meert, G Janssens Inductive Logic Programming: 24th International Conference, ILP 2014, Nancy …, 2015 | 28 | 2015 |
| Compiling probabilistic logic programs into sentential decision diagrams J Vlasselaer, J Renkens, G Van den Broeck, L De Raedt Proceedings Workshop on Probabilistic Logic Programming (PLP), 1-10, 2014 | 24 | 2014 |
| Knowledge Compilation and Weighted Model Counting for Inference in Probabilistic Logic Programs. J Vlasselaer, A Kimmig, A Dries, W Meert, L De Raedt AAAI Workshop: Beyond NP 101, 2016 | 18 | 2016 |
| ProbLog2: From probabilistic programming to statistical relational learning J Renkens, D Shterionov, G Van den Broeck, J Vlasselaer, D Fierens, ... Proceedings of the NIPS Probabilistic Programming Workshop, 2012 | 18 | 2012 |
| Efficient probabilistic inference for dynamic relational models J Vlasselaer, W Meert, G Van den Broeck, L De Raedt AAAI Workshop-Technical Report, 131-134, 2014 | 13 | 2014 |
| A relaxed tseitin transformation for weighted model counting W Meert, J Vlasselaer, G Van den Broeck Proceedings of the Sixth International Workshop on Statistical Relational AI …, 2016 | 9 | 2016 |
| Dynamic sensor-frontend tuning for resource efficient embedded classification L Galindez, K Badami, J Vlasselaer, W Meert, M Verhelst IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8 (4 …, 2018 | 8 | 2018 |
| Statistical relational learning for prognostics J Vlasselaer, W Meert, B De Baets, B Manderick, M Rademaker, ... Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, 45-50, 2012 | 8 | 2012 |
| Towards resource-efficient classifiers for always-on monitoring J Vlasselaer, W Meert, M Verhelst Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018 | 6 | 2018 |
| Condition monitoring with incomplete observations J Vlasselaer, W Meert, R Langone, L De Raedt, T Schaub, G Friedrich, ... 21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014) 263, 1215-+, 2014 | 3 | 2014 |
| BEHAVE-Behavioral analysis of visual events for assisted living scenarios C Fernando Crispim-Junior, J Vlasselaer, A Dries, F Bremond Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 1 | 2017 |
| Feature Noise Tuning for Resource Efficient Bayesian Network Classifiers LI Galindez Olascoaga, J Vlasselaer, W Meert, M Verhelst ESANN 2018 proceedings, European Symposium on Artificial Neural Networks …, 2018 | | 2018 |
| Dynamic Sensor-Frontend Tuning f L Galindez, K Badami, J Vlasselaer, W Meert, M Verhlest Citation Laura Galindez, Komail Badami, Jonas Vlasselaer, Wannes Meert …, 2018 | | 2018 |
| Feature noise tuning for resource efficient Bayesian Network Classifiers. LIG Olascoaga, J Vlasselaer, W Meert, M Verhelst ESANN, 2018 | | 2018 |
| Probabilistic Inference for Dynamic and Relational Models J Vlasselaer, L De Raedt | | 2016 |
