| Deep learning models for wireless signal classification with distributed low-cost spectrum sensors S Rajendran, W Meert, D Giustiniano, V Lenders, S Pollin IEEE Transactions on Cognitive Communications and Networking 4 (3), 433-445, 2018 | 986 | 2018 |
| Range and coexistence analysis of long range unlicensed communication B Reynders, W Meert, S Pollin 2016 23rd International Conference on Telecommunications (ICT), 1-6, 2016 | 344 | 2016 |
| Power and spreading factor control in low power wide area networks B Reynders, W Meert, S Pollin 2017 IEEE international conference on communications (ICC), 1-6, 2017 | 259 | 2017 |
| Lifted probabilistic inference by first-order knowledge compilation G Van den Broeck, N Taghipour, W Meert, J Davis, L De Raedt Proceedings ofthe 22th International Joint Conference on Artificial …, 2011 | 253 | 2011 |
| Machine learning with a reject option: A survey K Hendrickx, L Perini, D Van der Plas, W Meert, J Davis Machine Learning 113 (5), 3073-3110, 2024 | 207 | 2024 |
| Dtaidistance v2 W Meert, K Hendrickx, T Van Craenendonck, P Robberechts, H Blockeel, ... Zenodo (https://github.com/wannesm/dtaidistance), 2020 | 151* | 2020 |
| Code generation using machine learning: A systematic review E Dehaerne, B Dey, S Halder, S De Gendt, W Meert Ieee Access 10, 82434-82455, 2022 | 147 | 2022 |
| SessionShield: Lightweight protection against session hijacking N Nikiforakis, W Meert, Y Younan, M Johns, W Joosen International Symposium on Engineering Secure Software and Systems, 87-100, 2011 | 145 | 2011 |
| A 90 nm CMOS, $6\{\upmu {\text {W}}} $ power-proportional acoustic sensing frontend for voice activity detection KMH Badami, S Lauwereins, W Meert, M Verhelst IEEE Journal of Solid-State Circuits 51 (1), 291-302, 2015 | 142 | 2015 |
| Skolemization for weighted first-order model counting G Van den Broeck, W Meert, A Darwiche, C Baral, GD Giacomo, T Eiter Proceedings of the 14th International Conference on Principles of Knowledge …, 2014 | 133 | 2014 |
| Bitsquatting: Exploiting bit-flips for fun, or profit? N Nikiforakis, S Van Acker, W Meert, L Desmet, F Piessens, W Joosen Proceedings of the 22nd international conference on World Wide Web, 989-998, 2013 | 128 | 2013 |
| Semi-supervised anomaly detection with an application to water analytics V Vercruyssen, W Meert, G Verbruggen, K Maes, R Baumer, J Davis 2018 ieee international conference on data mining (icdm) 2018, 527-536, 2018 | 116 | 2018 |
| 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 |
| Unsupervised wireless spectrum anomaly detection with interpretable features S Rajendran, W Meert, V Lenders, S Pollin ieee transactions on cognitive communications and networking 5 (3), 637-647, 2019 | 109 | 2019 |
| SAIFE: Unsupervised wireless spectrum anomaly detection with interpretable features S Rajendran, W Meert, V Lenders, S Pollin 2018 IEEE international symposium on dynamic spectrum access networks …, 2018 | 94 | 2018 |
| 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 |
| A general anomaly detection framework for fleet-based condition monitoring of machines K Hendrickx, W Meert, Y Mollet, J Gyselinck, B Cornelis, K Gryllias, ... Mechanical Systems and Signal Processing 139, 106585, 2020 | 87 | 2020 |
| Pattern-based anomaly detection in mixed-type time series L Feremans, V Vercruyssen, B Cule, W Meert, B Goethals Joint European conference on machine learning and knowledge discovery in …, 2019 | 84 | 2019 |
| Fatigue prediction in outdoor runners via machine learning and sensor fusion T Op De Beéck, W Meert, K Schütte, B Vanwanseele, J Davis Proceedings of the 24th acm sigkdd international conference on knowledge …, 2018 | 84 | 2018 |
| Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned J Davis, L Bransen, L Devos, A Jaspers, W Meert, P Robberechts, ... Machine Learning 113 (9), 6977-7010, 2024 | 66 | 2024 |
Jesse DavisProfessor, Department of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
