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ORCIDWannes Meert
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- affiliation: KU Leuven, Department of Computer Science, Belgium
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2020 – today
- 2024
[j21]Daan Van Wesenbeeck
, Aras Yurtman, Wannes Meert, Hendrik Blockeel:
LoCoMotif: discovering time-warped motifs in time series. Data Min. Knowl. Discov. 38(4): 2276-2305 (2024)- [j20]Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis:
Machine learning with a reject option: a survey. Mach. Learn. 113(5): 3073-3110 (2024) - [j19]Arne De Brabandere, Tim Op De Beéck, Kilian Hendrickx, Wannes Meert, Jesse Davis:
TSFuse: automated feature construction for multiple time series data. Mach. Learn. 113(8): 5001-5056 (2024) - [j18]Jesse Davis, Lotte Bransen, Laurens Devos, Arne Jaspers, Wannes Meert, Pieter Robberechts, Jan Van Haaren, Maaike Van Roy:
Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned. Mach. Learn. 113(9): 6977-7010 (2024) - [j17]Wim Govers, Aras Yurtman, Turgay Aslandere, Nicole Eikelenberg, Wannes Meert, Jesse Davis:
Time-Shifted Transformers for Driver Identification Using Vehicle Data. IEEE Trans. Intell. Transp. Syst. 25(5): 3767-3776 (2024) - [c66]Louis Carpentier, Len Feremans, Wannes Meert, Mathias Verbeke:
Pattern-based Time Series Semantic Segmentation with Gradual State Transitions. SDM 2024: 316-324 - 2023
- [c65]Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis:
Detecting Evasion Attacks in Deployed Tree Ensembles. ECML/PKDD (5) 2023: 120-136 - [c64]Aras Yurtman, Jonas Soenen, Wannes Meert, Hendrik Blockeel:
Estimating Dynamic Time Warping Distance Between Time Series with Missing Data. ECML/PKDD (5) 2023: 221-237 - [c63]Dries Van der Pias, Wannes Meert, Johan Verbraecken, Jesse Davis:
A novel reject option applied to sleep stage scoring. SDM 2023: 820-828 - [i24]Jonas Soenen, Elia Van Wolputte, Vincent Vercruyssen, Wannes Meert, Hendrik Blockeel:
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection. CoRR abs/2305.12958 (2023) - [i23]Sieben Bocklandt, Wannes Meert, Koen Vanderstraeten, Wouter Pijpops, Kurt Jaspers:
Deriving Comprehensible Theories from Probabilistic Circuits. CoRR abs/2311.13379 (2023) - [i22]Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert, Hendrik Blockeel:
LoCoMotif: Discovering time-warped motifs in time series. CoRR abs/2311.17582 (2023) - [i21]Enrique Dehaerne, Bappaditya Dey, Wannes Meert:
A Machine Learning Approach Towards SKILL Code Autocompletion. CoRR abs/2312.01921 (2023) - 2022
- [j16]Enrique Dehaerne, Bappaditya Dey, Sandip Halder, Stefan De Gendt, Wannes Meert:
Code Generation Using Machine Learning: A Systematic Review. IEEE Access 10: 82434-82455 (2022) - [j15]Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst:
DPU: DAG Processing Unit for Irregular Graphs With Precision-Scalable Posit Arithmetic in 28 nm. IEEE J. Solid State Circuits 57(8): 2586-2596 (2022) - [j14]Nimish Shah, Wannes Meert, Marian Verhelst:
GraphOpt: Constrained-Optimization-Based Parallelization of Irregular Graphs. IEEE Trans. Parallel Distributed Syst. 33(12): 3321-3332 (2022) - [c62]Kshitij Goyal, Wannes Meert, Hendrik Blockeel, Elia Van Wolputte, Koen Vanderstraeten, Wouter Pijpops, Kurt Jaspers:
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming. BNAIC/BENELEARN 2022: 47-64 - [c61]Shirui Zhao, Nimish Shah, Wannes Meert, Marian Verhelst:
Discrete Samplers for Approximate Inference in Probabilistic Machine Learning. DATE 2022: 1221-1226 - [c60]Pieter Robberechts, Wannes Meert, Jesse Davis:
Elastic Product Quantization for Time Series. DS 2022: 157-172 - [c59]Wen-Chi Yang, Arcchit Jain, Luc De Raedt, Wannes Meert:
Parameter Learning in ProbLog with Annotated Disjunctions. IDA 2022: 378-391 - [c58]Jesse Davis, Lotte Bransen, Laurens Devos, Wannes Meert, Pieter Robberechts, Jan Van Haaren, Maaike Van Roy:
Evaluating Sports Analytics Models: Challenges, Approaches, and Lessons Learned. EBeM@IJCAI 2022 - [c57]Nimish Shah, Wannes Meert, Marian Verhelst:
DPU-v2: Energy-efficient execution of irregular directed acyclic graphs. MICRO 2022: 1288-1307 - [c56]Loren Nuyts, Laurens Devos, Wannes Meert, Jesse Davis:
Bitpaths: Compressing Datasets Without Decreasing Predictive Performance. PKDD/ECML Workshops (1) 2022: 261-268 - [c55]Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis:
Multi-domain Active Learning for Semi-supervised Anomaly Detection. ECML/PKDD (4) 2022: 485-501 - [i20]Pieter Robberechts, Wannes Meert, Jesse Davis:
Elastic Product Quantization for Time Series. CoRR abs/2201.01856 (2022) - [i19]Laurens Devos, Wannes Meert, Jesse Davis:
Adversarial Example Detection in Deployed Tree Ensembles. CoRR abs/2206.13083 (2022) - [i18]Kshitij Goyal, Wannes Meert, Hendrik Blockeel, Elia Van Wolputte, Koen Vanderstraeten, Wouter Pijpops, Kurt Jaspers:
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming. CoRR abs/2210.01515 (2022) - [i17]Nimish Shah, Wannes Meert, Marian Verhelst:
DPU-v2: Energy-efficient execution of irregular directed acyclic graphs. CoRR abs/2210.13184 (2022) - 2021
- [j13]Dries Van der Plas, Johan Verbraecken, Marc Willemen, Wannes Meert, Jesse Davis:
Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies. Frontiers Digit. Health 3: 707589 (2021) - [c54]Sebastijan Dumancic, Wannes Meert, Stijn Goethals, Tim Stuyckens, Jelle Huygen, Koen Denies:
Automated Reasoning and Learning for Automated Payroll Management. AAAI 2021: 15107-15116 - [c53]Dries Van Daele, Nicholas Decleyre, Herman Dubois, Wannes Meert:
An Automated Engineering Assistant: Learning Parsers for Technical Drawings. AAAI 2021: 15195-15203 - [c52]Kilian Hendrickx, Wannes Meert, Bram Cornelis, Jesse Davis:
Know Your Limits: Machine Learning with Rejection for Vehicle Engineering. ADMA 2021: 273-288 - [c51]Laurens Devos, Wannes Meert, Jesse Davis:
Versatile Verification of Tree Ensembles. ICML 2021: 2654-2664 - [c50]Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst:
9.4 PIU: A 248GOPS/W Stream-Based Processor for Irregular Probabilistic Inference Networks Using Precision-Scalable Posit Arithmetic in 28nm. ISSCC 2021: 150-152 - [c49]Laurens Devos, Wannes Meert, Jesse Davis:
Verifying Tree Ensembles by Reasoning about Potential Instances. SDM 2021: 450-458 - [i16]Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst:
ProbLP: A framework for low-precision probabilistic inference. CoRR abs/2103.00216 (2021) - [i15]Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst:
Acceleration of probabilistic reasoning through custom processor architecture. CoRR abs/2103.00266 (2021) - [i14]Nimish Shah, Wannes Meert, Marian Verhelst:
GRAPHOPT: constrained optimization-based parallelization of irregular graphs. CoRR abs/2105.01976 (2021) - [i13]Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis:
Machine Learning with a Reject Option: A survey. CoRR abs/2107.11277 (2021) - [i12]Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst:
DPU: DAG Processing Unit for Irregular Graphs with Precision-Scalable Posit Arithmetic in 28nm. CoRR abs/2112.05660 (2021) - 2020
- [j12]Sreeraj Rajendran, Vincent Lenders, Wannes Meert, Sofie Pollin:
Crowdsourced Wireless Spectrum Anomaly Detection. IEEE Trans. Cogn. Commun. Netw. 6(2): 694-703 (2020) - [c48]Vincent Vercruyssen, Wannes Meert, Jesse Davis:
Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection. AAAI 2020: 6054-6061 - [c47]Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst:
Acceleration of probabilistic reasoning through custom processor architecture. DATE 2020: 322-325 - [c46]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, Marian Verhelst:
Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams. IDA 2020: 184-196 - [c45]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst:
Dynamic Complexity Tuning for Hardware-Aware Probabilistic Circuits. IoT Streams/ITEM@PKDD/ECML 2020: 283-295 - [c44]Vincent Vercruyssen, Wannes Meert, Jesse Davis:
"Now you see it, now you don't!" Detecting Suspicious Pattern Absences in Continuous Time Series. SDM 2020: 127-135 - [d1]Wannes Meert, Kilian Hendrickx, Toon van Craenendonck, Pieter Robberechts, Hendrik Blockeel, Jesse Davis:
DTAIDistance. Zenodo, 2020
- [i11]Laurens Devos, Wannes Meert, Jesse Davis:
Additive Tree Ensembles: Reasoning About Potential Instances. CoRR abs/2001.11905 (2020) - [i10]Laurens Devos, Wannes Meert, Jesse Davis:
Versatile Verification of Tree Ensembles. CoRR abs/2010.13880 (2020)
2010 – 2019
- 2019
- [j11]Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie Pollin:
Unsupervised Wireless Spectrum Anomaly Detection With Interpretable Features. IEEE Trans. Cogn. Commun. Netw. 5(3): 637-647 (2019) - [c43]Mathias Van Herreweghe, Mathias Verbeke, Wannes Meert, Tom Jacobs:
A Machine Learning-Based Approach for Predicting Tool Wear in Industrial Milling Processes. BNAIC/BENELEARN 2019 - [c42]Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst:
ProbLP: A framework for low-precision probabilistic inference. DAC 2019: 190 - [c41]Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel:
Learning Relational Representations with Auto-encoding Logic Programs. IJCAI 2019: 6081-6087 - [c40]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, Marian Verhelst:
On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications. EMC2@NeurIPS 2019: 66-70 - [c39]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck:
Towards Hardware-Aware Tractable Learning of Probabilistic Models. NeurIPS 2019: 13726-13736 - [c38]Len Feremans, Vincent Vercruyssen, Wannes Meert, Boris Cule, Bart Goethals:
A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs. NFMCP@PKDD/ECML 2019: 3-20 - [c37]Dries Van Daele, Nicholas Decleyre, Herman Dubois, Wannes Meert:
Learning Parsers for Technical Drawings. PKDD/ECML Workshops (1) 2019: 51-56 - [c36]Len Feremans, Vincent Vercruyssen, Boris Cule, Wannes Meert, Bart Goethals:
Pattern-Based Anomaly Detection in Mixed-Type Time Series. ECML/PKDD (1) 2019: 240-256 - [c35]Mathias Van Herreweghe, Mathias Verbeke, Wannes Meert, Tom Jacobs:
A Machine Learning-Based Approach for Predicting Tool Wear in Industrial Milling Processes. PKDD/ECML Workshops (2) 2019: 414-425 - [c34]Laurens Devos, Wannes Meert, Jesse Davis:
Fast Gradient Boosting Decision Trees with Bit-Level Data Structures. ECML/PKDD (1) 2019: 590-606 - [i9]Sreeraj Rajendran, Vincent Lenders, Wannes Meert, Sofie Pollin:
Crowdsourced wireless spectrum anomaly detection. CoRR abs/1903.05408 (2019) - [i8]Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel:
Learning Relational Representations with Auto-encoding Logic Programs. CoRR abs/1903.12577 (2019) - [i7]Dries Van Daele, Nicholas Decleyre, Herman Dubois, Wannes Meert:
An Automated Engineering Assistant: Learning Parsers for Technical Drawings. CoRR abs/1909.08552 (2019) - [i6]Kilian Hendrickx, Wannes Meert, Yves Mollet, Johan Gyselinck, Bram Cornelis, Konstantinos C. Gryllias, Jesse Davis:
A general anomaly detection framework for fleet-based condition monitoring of machines. CoRR abs/1912.12941 (2019) - 2018
- [j10]Laura Isabel Galindez Olascoaga, Komail M. H. Badami, Jonas Vlasselaer, Wannes Meert, Marian Verhelst:
Dynamic Sensor-Frontend Tuning for Resource Efficient Embedded Classification. IEEE J. Emerg. Sel. Topics Circuits Syst. 8(4): 858-872 (2018) - [j9]Sreeraj Rajendran, Wannes Meert, Domenico Giustiniano, Vincent Lenders, Sofie Pollin:
Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors. IEEE Trans. Cogn. Commun. Netw. 4(3): 433-445 (2018) - [c33]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
COBRASTS: A New Approach to Semi-supervised Clustering of Time Series. DS 2018: 179-193 - [c32]Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie Pollin:
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features. DySPAN 2018: 1-9 - [c31]Laura Isabel Galindez Olascoaga, Jonas Vlasselaer, Wannes Meert, Marian Verhelst:
Feature noise tuning for resource efficient Bayesian Network Classifiers. ESANN 2018 - [c30]Vincent Vercruyssen, Wannes Meert, Gust Verbruggen, Koen Maes, Ruben Baumer, Jesse Davis:
Semi-Supervised Anomaly Detection with an Application to Water Analytics. ICDM 2018: 527-536 - [c29]Tim Op De Beéck, Wannes Meert, Kurt Schütte, Benedicte Vanwanseele, Jesse Davis:
Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. KDD 2018: 606-615 - [c28]Pieter Robberechts, Maarten Bosteels, Jesse Davis, Wannes Meert:
Query Log Analysis: Detecting Anomalies in DNS Traffic at a TLD Resolver. DMLE/IOTSTREAMING@PKDD/ECML 2018: 55-67 - [c27]Jonas Vlasselaer, Wannes Meert, Marian Verhelst:
Towards Resource-Efficient Classifiers for Always-On Monitoring. ECML/PKDD (3) 2018: 305-321 - [c26]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
Interactive Time Series Clustering with COBRASTS. ECML/PKDD (3) 2018: 654-657 - [i5]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series. CoRR abs/1805.00779 (2018) - 2017
- [c25]Brecht Reynders, Wannes Meert, Sofie Pollin:
Power and spreading factor control in low power wide area networks. ICC 2017: 1-6 - [c24]Vincent Vercruyssen, Wannes Meert, Jesse Davis:
Transfer Learning for Time Series Anomaly Detection. IAL@PKDD/ECML 2017: 27-36 - [i4]Sreeraj Rajendran, Wannes Meert, Domenico Giustiniano, Vincent Lenders, Sofie Pollin:
Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors. CoRR abs/1707.08908 (2017) - 2016
- [j8]Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt:
Exploiting local and repeated structure in Dynamic Bayesian Networks. Artif. Intell. 232: 43-53 (2016) - [j7]Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt:
TP-Compilation for inference in probabilistic logic programs. Int. J. Approx. Reason. 78: 15-32 (2016) - [j6]Komail M. H. Badami, Steven Lauwereins, Wannes Meert, Marian Verhelst:
A 90 nm CMOS, 6µW Power-Proportional Acoustic Sensing Frontend for Voice Activity Detection. IEEE J. Solid State Circuits 51(1): 291-302 (2016) - [j5]Jan Van Haaren, Guy Van den Broeck, Wannes Meert, Jesse Davis:
Lifted generative learning of Markov logic networks. Mach. Learn. 103(1): 27-55 (2016) - [c23]Jonas Vlasselaer, Angelika Kimmig, Anton Dries, Wannes Meert, Luc De Raedt:
Knowledge Compilation and Weighted Model Counting for Inference in Probabilistic Logic Programs. AAAI Workshop: Beyond NP 2016 - [c22]Laura Isabel Galindez Olascoaga, Komail M. H. Badami, V. Rajesh Pamula, Steven Lauwereins, Wannes Meert, Marian Verhelst:
Exploiting system configurability towards dynamic accuracy-power trade-offs in sensor front-ends. ACSSC 2016: 1027-1031 - [c21]Laura Isabel Galindez Olascoaga, Wannes Meert, Herman Bruyninckx, Marian Verhelst:
Extending Naive Bayes with Precision-tunable Feature Variables for Resource-efficient Sensor Fusion. AI-IoT@ECAI 2016: 23-30 - [c20]Brecht Reynders, Wannes Meert, Sofie Pollin:
Range and coexistence analysis of long range unlicensed communication. ICT 2016: 1-6 - [c19]Christiaan Leysen, Mathias Verbeke, Pierre Dagnely, Wannes Meert:
Energy consumption profiling using Gaussian processes. IEEE Conf. on Intelligent Systems 2016: 470-477 - [i3]Sebastijan Dumancic, Wannes Meert, Hendrik Blockeel:
Theory reconstruction: a representation learning view on predicate invention. CoRR abs/1606.08660 (2016) - 2015
- [j4]Rocco Langone, Carlos Alzate, Bart De Ketelaere, Jonas Vlasselaer, Wannes Meert, Johan A. K. Suykens:
LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. Eng. Appl. Artif. Intell. 37: 268-278 (2015) - [j3]Steven Lauwereins, Komail M. H. Badami, Wannes Meert, Marian Verhelst:
Optimal resource usage in ultra-low-power sensor interfaces through context- and resource-cost-aware machine learning. Neurocomputing 169: 236-245 (2015) - [c18]Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt:
Anytime Inference in Probabilistic Logic Programs with Tp-Compilation. IJCAI 2015: 1852-1858 - [c17]Komail M. H. Badami, Steven Lauwereins, Wannes Meert, Marian Verhelst:
24.2 Context-aware hierarchical information-sensing in a 6μW 90nm CMOS voice activity detector. ISSCC 2015: 1-3 - [c16]Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer, Luc De Raedt:
ProbLog2: Probabilistic Logic Programming. ECML/PKDD (3) 2015: 312-315 - 2014
- [c15]Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt:
Efficient Probabilistic Inference for Dynamic Relational Models. StarAI@AAAI 2014 - [c14]Jonas Vlasselaer, Wannes Meert, Rocco Langone, Luc De Raedt:
Condition Monitoring with Incomplete Observations. ECAI 2014: 1215-1216 - [c13]Steven Lauwereins, Komail M. H. Badami, Wannes Meert, Marian Verhelst:
Context- and cost-aware feature selection in ultra-low-power sensor interfaces. ESANN 2014 - [c12]Dimitar Sht. Shterionov, Joris Renkens, Jonas Vlasselaer, Angelika Kimmig, Wannes Meert, Gerda Janssens:
The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions. ILP 2014: 139-153 - [c11]Guy Van den Broeck, Wannes Meert, Adnan Darwiche:
Skolemization for Weighted First-Order Model Counting. KR 2014 - [c10]Steven Lauwereins, Wannes Meert, Jort F. Gemmeke, Marian Verhelst:
Ultra-low-power voice-activity-detector through context- and resource-cost-aware feature selection in decision trees. MLSP 2014: 1-6 - [c9]Wannes Meert, Joost Vennekens:
Inhibited Effects in CP-Logic. Probabilistic Graphical Models 2014: 350-365 - 2013
- [c8]Guy Van den Broeck, Wannes Meert, Jesse Davis:
Lifted Generative Parameter Learning. StarAI@AAAI 2013 - [c7]Lieven Billiet, José Oramas M., McElory Hoffmann, Wannes Meert, Laura Antanas:
Rule-based Hand Posture Recognition using Qualitative Finger Configurations Acquired with the Kinect. ICPRAM 2013: 539-542 - [c6]Nick Nikiforakis, Steven Van Acker, Wannes Meert, Lieven Desmet, Frank Piessens, Wouter Joosen:
Bitsquatting: exploiting bit-flips for fun, or profit? WWW 2013: 989-998 - [i2]Guy Van den Broeck, Wannes Meert, Adnan Darwiche:
Skolemization for Weighted First-Order Model Counting. CoRR abs/1312.5378 (2013) - 2011
- [b1]Wannes Meert:
Inference and Learning for Directed Probabilistic Logic Models (Inferentie en leren voor gerichte probabilistische logische modellen). Katholieke Universiteit Leuven, Belgium, 2011 - [c5]Nick Nikiforakis, Wannes Meert, Yves Younan, Martin Johns, Wouter Joosen:
SessionShield: Lightweight Protection against Session Hijacking. ESSoS 2011: 87-100 - [c4]Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis, Luc De Raedt:
Lifted Probabilistic Inference by First-Order Knowledge Compilation. IJCAI 2011: 2178-2185 - 2010
- [j2]Jon Sneyers, Wannes Meert, Joost Vennekens, Yoshitaka Kameya, Taisuke Sato:
CHR(PRISM)-based probabilistic logic learning. Theory Pract. Log. Program. 10(4-6): 433-447 (2010) - [c3]Wannes Meert, Nima Taghipour, Hendrik Blockeel:
First-Order Bayes-Ball. ECML/PKDD (2) 2010: 369-384 - [i1]Jon Sneyers, Wannes Meert, Joost Vennekens, Yoshitaka Kameya, Taisuke Sato:
CHR(PRISM)-based Probabilistic Logic Learning. CoRR abs/1007.3858 (2010)
2000 – 2009
- 2009
- [c2]Wannes Meert, Jan Struyf, Hendrik Blockeel:
CP-Logic Theory Inference with Contextual Variable Elimination and Comparison to BDD Based Inference Methods. ILP 2009: 96-109 - 2008
- [j1]Wannes Meert, Jan Struyf, Hendrik Blockeel:
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques. Fundam. Informaticae 89(1): 131-160 (2008) - 2006
- [c1]Hendrik Blockeel, Wannes Meert:
Towards Learning Non-recursive LPADs by Transforming Them into Bayesian Networks. ILP 2006: 94-108
Coauthor Index