Jonas Vlasselaer
Jonas Vlasselaer
PhD researcher KU Leuven
Verified email at cs.kuleuven.be
Title
Cited by
Cited by
Year
Probabilistic sentential decision diagrams
D Kisa, G Van den Broeck, A Choi, A Darwiche
Fourteenth International Conference on the Principles of Knowledge …, 2014
732014
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
602015
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
382015
Anytime inference in probabilistic logic programs with Tp-compilation
J Vlasselaer, G Van den Broeck, A Kimmig, W Meert, L De Raedt
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
302015
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
272016
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
222016
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
132014
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
102012
Efficient probabilistic inference for dynamic relational models
J Vlasselaer, W Meert, G Van den Broeck, L De Raedt
Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014
92014
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, 139-153, 2015
72015
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
52018
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
42016
Knowledge compilation and weighted model counting for inference in probabilistic logic programs
J Vlasselaer, A Kimmig, A Dries, W Meert, L De Raedt
Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, 2016
32016
Statistical relational learning for prognostics
J Vlasselaer, W Meert
Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, 45-50, 2012
32012
Condition monitoring with incomplete observations
J Vlasselaer, W Meert, R Langone, L De Raedt
21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014) 263, 1215-+, 2014
22014
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
12018
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
12017
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
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Articles 1–20