Towards Learning Non-recursive LPADs by Transforming Them into Bayesian Networks. - Hendrik Blockeel; Wannes Meert (ILP, 2006)

Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques. - Wannes Meert; Jan Struyf; Hendrik Blockeel (Fundam. Informaticae, 2008)

CP-Logic Theory Inference with Contextual Variable Elimination and Comparison to BDD Based Inference Methods. - Wannes Meert; Jan Struyf; Hendrik Blockeel (ILP, 2009)

An Exercise with Statistical Relational Learning Systems. - Maurice Bruynooghe; Daan Fierens; Bernd Gutmann; Angelika Kimmig; Niels Landwehr; Wannes Meert; Ingo Thon; Luc De Raedt (MLG/SRL@ILP, 2009)

First-Order Bayes-Ball for CP-Logic. - Nima Taghipour; Wannes Meert; Jan Struyf; Hendrik Blockeel (MLG/SRL@ILP, 2009)

First-Order Bayes-Ball. - Wannes Meert; Nima Taghipour; Hendrik Blockeel (ECML/PKDD (2), 2010)

CHR(PRISM)-based probabilistic logic learning. - Jon Sneyers; Wannes Meert; Joost Vennekens; Yoshitaka Kameya; Taisuke Sato (Theory Pract. Log. Program., 2010)

SessionShield: Lightweight Protection against Session Hijacking. - Nick Nikiforakis; Wannes Meert; Yves Younan; Martin Johns; Wouter Joosen (ESSoS, 2011)

Lifted Probabilistic Inference by First-Order Knowledge Compilation. - Guy Van den Broeck; Nima Taghipour; Wannes Meert; Jesse Davis; Luc De Raedt (IJCAI, 2011)

Inference and Learning for Directed Probabilistic Logic Models (Inferentie en leren voor gerichte probabilistische logische modellen). - Wannes Meert (2011)

Lifted Generative Parameter Learning. - Guy Van den Broeck; Wannes Meert; Jesse Davis (StarAI@AAAI, 2013)

Rule-based Hand Posture Recognition using Qualitative Finger Configurations Acquired with the Kinect. - Lieven Billiet; José Oramas M.; McElory Hoffmann; Wannes Meert; Laura Antanas (ICPRAM, 2013)

Bitsquatting: exploiting bit-flips for fun, or profit? - Nick Nikiforakis; Steven Van Acker; Wannes Meert; Lieven Desmet; Frank Piessens; Wouter Joosen (WWW, 2013)

Efficient Probabilistic Inference for Dynamic Relational Models. - Jonas Vlasselaer; Wannes Meert; Guy Van den Broeck; Luc De Raedt (StarAI@AAAI, 2014)

Condition Monitoring with Incomplete Observations. - Jonas Vlasselaer; Wannes Meert; Rocco Langone; Luc De Raedt (ECAI, 2014)

Context- and cost-aware feature selection in ultra-low-power sensor interfaces. - Steven Lauwereins; Komail M. H. Badami; Wannes Meert; Marian Verhelst (ESANN, 2014)

The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions. - Dimitar Sht. Shterionov; Joris Renkens; Jonas Vlasselaer; Angelika Kimmig; Wannes Meert; Gerda Janssens (ILP, 2014)

Skolemization for Weighted First-Order Model Counting. - Guy Van den Broeck; Wannes Meert; Adnan Darwiche (KR, 2014)

Ultra-low-power voice-activity-detector through context- and resource-cost-aware feature selection in decision trees. - Steven Lauwereins; Wannes Meert; Jort F. Gemmeke; Marian Verhelst (MLSP, 2014)

Inhibited Effects in CP-Logic. - Wannes Meert; Joost Vennekens (Probabilistic Graphical Models, 2014)

Anytime Inference in Probabilistic Logic Programs with Tp-Compilation. - Jonas Vlasselaer; Guy Van den Broeck; Angelika Kimmig; Wannes Meert; Luc De Raedt (IJCAI, 2015)

24.2 Context-aware hierarchical information-sensing in a 6μW 90nm CMOS voice activity detector. - Komail M. H. Badami; Steven Lauwereins; Wannes Meert; Marian Verhelst (ISSCC, 2015)

ProbLog2: Probabilistic Logic Programming. - Anton Dries; Angelika Kimmig; Wannes Meert; Joris Renkens; Guy Van den Broeck; Jonas Vlasselaer; Luc De Raedt (ECML/PKDD (3), 2015)

LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. - Rocco Langone; Carlos Alzate; Bart De Ketelaere; Jonas Vlasselaer; Wannes Meert; Johan A. K. Suykens (Eng. Appl. Artif. Intell., 2015)

Optimal resource usage in ultra-low-power sensor interfaces through context- and resource-cost-aware machine learning. - Steven Lauwereins; Komail M. H. Badami; Wannes Meert; Marian Verhelst (Neurocomputing, 2015)

Knowledge Compilation and Weighted Model Counting for Inference in Probabilistic Logic Programs. - Jonas Vlasselaer; Angelika Kimmig; Anton Dries; Wannes Meert; Luc De Raedt (AAAI Workshop: Beyond NP, 2016)

Exploiting system configurability towards dynamic accuracy-power trade-offs in sensor front-ends. - Laura Isabel Galindez Olascoaga; Komail M. H. Badami; V. Rajesh Pamula 002; Steven Lauwereins; Wannes Meert; Marian Verhelst (ACSSC, 2016)

Extending Naive Bayes with Precision-tunable Feature Variables for Resource-efficient Sensor Fusion. - Laura Isabel Galindez Olascoaga; Wannes Meert; Herman Bruyninckx; Marian Verhelst (AI-IoT@ECAI, 2016)

Range and coexistence analysis of long range unlicensed communication. - Brecht Reynders; Wannes Meert; Sofie Pollin (ICT, 2016)

Energy consumption profiling using Gaussian processes. - Christiaan Leysen; Mathias Verbeke; Pierre Dagnely; Wannes Meert (IEEE Conf. on Intelligent Systems, 2016)

Exploiting local and repeated structure in Dynamic Bayesian Networks. - Jonas Vlasselaer; Wannes Meert; Guy Van den Broeck; Luc De Raedt (Artif. Intell., 2016)

Theory reconstruction: a representation learning view on predicate invention. - Sebastijan Dumancic; Wannes Meert; Hendrik Blockeel (CoRR, 2016)

TP-Compilation for inference in probabilistic logic programs. - Jonas Vlasselaer; Guy Van den Broeck; Angelika Kimmig; Wannes Meert; Luc De Raedt (Int. J. Approx. Reason., 2016)

A 90 nm CMOS, 6µW Power-Proportional Acoustic Sensing Frontend for Voice Activity Detection. - Komail M. H. Badami; Steven Lauwereins; Wannes Meert; Marian Verhelst (IEEE J. Solid State Circuits, 2016)

Lifted generative learning of Markov logic networks. - Jan Van Haaren; Guy Van den Broeck; Wannes Meert; Jesse Davis (Mach. Learn., 2016)

Power and spreading factor control in low power wide area networks. - Brecht Reynders; Wannes Meert; Sofie Pollin (ICC, 2017)

Transfer Learning for Time Series Anomaly Detection. - Vincent Vercruyssen; Wannes Meert; Jesse Davis (IAL@PKDD/ECML, 2017)

Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors. - Sreeraj Rajendran; Wannes Meert; Domenico Giustiniano; Vincent Lenders; Sofie Pollin (CoRR, 2017)

COBRASTS: A New Approach to Semi-supervised Clustering of Time Series. - Toon van Craenendonck; Wannes Meert; Sebastijan Dumancic; Hendrik Blockeel (DS, 2018)

SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features. - Sreeraj Rajendran; Wannes Meert; Vincent Lenders; Sofie Pollin (DySPAN, 2018)

Feature noise tuning for resource efficient Bayesian Network Classifiers. - Laura Isabel Galindez Olascoaga; Jonas Vlasselaer; Wannes Meert; Marian Verhelst (ESANN, 2018)

Semi-Supervised Anomaly Detection with an Application to Water Analytics. - Vincent Vercruyssen; Wannes Meert; Gust Verbruggen; Koen Maes; Ruben Baumer; Jesse Davis (ICDM, 2018)

Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. - Tim Op De Beéck; Wannes Meert; Kurt Schütte 002; Benedicte Vanwanseele; Jesse Davis (KDD, 2018)

Interactive Time Series Clustering with COBRASTS. - Toon van Craenendonck; Wannes Meert; Sebastijan Dumancic; Hendrik Blockeel (ECML/PKDD (3), 2018)

Query Log Analysis: Detecting Anomalies in DNS Traffic at a TLD Resolver. - Pieter Robberechts; Maarten Bosteels; Jesse Davis; Wannes Meert (DMLE/IOTSTREAMING@PKDD/ECML, 2018)

Towards Resource-Efficient Classifiers for Always-On Monitoring. - Jonas Vlasselaer; Wannes Meert; Marian Verhelst (ECML/PKDD (3), 2018)

COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series. - Toon van Craenendonck; Wannes Meert; Sebastijan Dumancic; Hendrik Blockeel (CoRR, 2018)

Dynamic Sensor-Frontend Tuning for Resource Efficient Embedded Classification. - Laura Isabel Galindez Olascoaga; Komail M. H. Badami; Jonas Vlasselaer; Wannes Meert; Marian Verhelst (IEEE J. Emerg. Sel. Topics Circuits Syst., 2018)

Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors. - Sreeraj Rajendran; Wannes Meert; Domenico Giustiniano; Vincent Lenders; Sofie Pollin (IEEE Trans. Cogn. Commun. Netw., 2018)

ProbLP: A framework for low-precision probabilistic inference. - Nimish Shah; Laura Isabel Galindez Olascoaga; Wannes Meert; Marian Verhelst (DAC, 2019)

Learning Relational Representations with Auto-encoding Logic Programs. - Sebastijan Dumancic; Tias Guns; Wannes Meert; Hendrik Blockeel (IJCAI, 2019)

On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications. - Laura Isabel Galindez Olascoaga; Wannes Meert; Nimish Shah; Guy Van den Broeck; Marian Verhelst (EMC2@NeurIPS, 2019)

Towards Hardware-Aware Tractable Learning of Probabilistic Models. - Laura Isabel Galindez Olascoaga; Wannes Meert; Nimish Shah; Marian Verhelst; Guy Van den Broeck (NeurIPS, 2019)

Learning Parsers for Technical Drawings. - Dries Van Daele; Nicholas Decleyre; Herman Dubois; Wannes Meert (PKDD/ECML Workshops (1), 2019)

Fast Gradient Boosting Decision Trees with Bit-Level Data Structures. - Laurens Devos; Wannes Meert; Jesse Davis (ECML/PKDD (1), 2019)

Pattern-Based Anomaly Detection in Mixed-Type Time Series. - Len Feremans; Vincent Vercruyssen; Boris Cule; Wannes Meert; Bart Goethals (ECML/PKDD (1), 2019)

A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs. - Len Feremans; Vincent Vercruyssen; Wannes Meert; Boris Cule; Bart Goethals (NFMCP@PKDD/ECML, 2019)

A Machine Learning-Based Approach for Predicting Tool Wear in Industrial Milling Processes. - Mathias Van Herreweghe; Mathias Verbeke; Wannes Meert; Tom Jacobs (PKDD/ECML Workshops (2), 2019)

A general anomaly detection framework for fleet-based condition monitoring of machines. - Kilian Hendrickx; Wannes Meert; Yves Mollet; Johan Gyselinck; Bram Cornelis; Konstantinos C. Gryllias; Jesse Davis (CoRR, 2019)

Unsupervised Wireless Spectrum Anomaly Detection With Interpretable Features. - Sreeraj Rajendran; Wannes Meert; Vincent Lenders; Sofie Pollin (IEEE Trans. Cogn. Commun. Netw., 2019)

Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection. - Vincent Vercruyssen; Wannes Meert; Jesse Davis (AAAI, 2020)

Acceleration of probabilistic reasoning through custom processor architecture. - Nimish Shah; Laura Isabel Galindez Olascoaga; Wannes Meert; Marian Verhelst (DATE, 2020)

Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams. - Laura Isabel Galindez Olascoaga; Wannes Meert; Nimish Shah; Guy Van den Broeck; Marian Verhelst (IDA, 2020)

Dynamic Complexity Tuning for Hardware-Aware Probabilistic Circuits. - Laura Isabel Galindez Olascoaga; Wannes Meert; Nimish Shah; Marian Verhelst (IoT Streams/ITEM@PKDD/ECML, 2020)

"Now you see it, now you don't!" Detecting Suspicious Pattern Absences in Continuous Time Series. - Vincent Vercruyssen; Wannes Meert; Jesse Davis (SDM, 2020)

Additive Tree Ensembles: Reasoning About Potential Instances. - Laurens Devos; Wannes Meert; Jesse Davis (CoRR, 2020)

Crowdsourced Wireless Spectrum Anomaly Detection. - Sreeraj Rajendran; Vincent Lenders; Wannes Meert; Sofie Pollin (IEEE Trans. Cogn. Commun. Netw., 2020)

DTAIDistance. - Wannes Meert; Kilian Hendrickx; Toon van Craenendonck; Pieter Robberechts; Hendrik Blockeel; Jesse Davis (Zenodo, 2020)

An Automated Engineering Assistant: Learning Parsers for Technical Drawings. - Dries Van Daele; Nicholas Decleyre; Herman Dubois; Wannes Meert (AAAI, 2021)

Automated Reasoning and Learning for Automated Payroll Management. - Sebastijan Dumancic; Wannes Meert; Stijn Goethals; Tim Stuyckens; Jelle Huygen; Koen Denies (AAAI, 2021)

Know Your Limits: Machine Learning with Rejection for Vehicle Engineering. - Kilian Hendrickx; Wannes Meert; Bram Cornelis; Jesse Davis (ADMA, 2021)

Versatile Verification of Tree Ensembles. - Laurens Devos; Wannes Meert; Jesse Davis (ICML, 2021)

9.4 PIU: A 248GOPS/W Stream-Based Processor for Irregular Probabilistic Inference Networks Using Precision-Scalable Posit Arithmetic in 28nm. - Nimish Shah; Laura Isabel Galindez Olascoaga; Shirui Zhao; Wannes Meert; Marian Verhelst (ISSCC, 2021)

Verifying Tree Ensembles by Reasoning about Potential Instances. - Laurens Devos; Wannes Meert; Jesse Davis (SDM, 2021)

DPU: DAG Processing Unit for Irregular Graphs with Precision-Scalable Posit Arithmetic in 28nm. - Nimish Shah; Laura Isabel Galindez Olascoaga; Shirui Zhao; Wannes Meert; Marian Verhelst (CoRR, 2021)

Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies. - Dries Van der Plas; Johan Verbraecken; Marc Willemen; Wannes Meert; Jesse Davis (Frontiers Digit. Health, 2021)

Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming. - Kshitij Goyal; Wannes Meert; Hendrik Blockeel; Elia Van Wolputte; Koen Vanderstraeten; Wouter Pijpops; Kurt Jaspers (BNAIC/BENELEARN, 2022)

Discrete Samplers for Approximate Inference in Probabilistic Machine Learning. - Shirui Zhao; Nimish Shah; Wannes Meert; Marian Verhelst (DATE, 2022)

Elastic Product Quantization for Time Series. - Pieter Robberechts; Wannes Meert; Jesse Davis (DS, 2022)

Parameter Learning in ProbLog with Annotated Disjunctions. - Wen-Chi Yang; Arcchit Jain; Luc De Raedt; Wannes Meert (IDA, 2022)

Evaluating Sports Analytics Models: Challenges, Approaches, and Lessons Learned. - Jesse Davis; Lotte Bransen; Laurens Devos; Wannes Meert; Pieter Robberechts; Jan Van Haaren; Maaike Van Roy (EBeM@IJCAI, 2022)

DPU-v2: Energy-efficient execution of irregular directed acyclic graphs. - Nimish Shah; Wannes Meert; Marian Verhelst (MICRO, 2022)

Bitpaths: Compressing Datasets Without Decreasing Predictive Performance. - Loren Nuyts; Laurens Devos; Wannes Meert; Jesse Davis (PKDD/ECML Workshops (1), 2022)

Multi-domain Active Learning for Semi-supervised Anomaly Detection. - Vincent Vercruyssen; Lorenzo Perini; Wannes Meert; Jesse Davis (ECML/PKDD (4), 2022)

Code Generation Using Machine Learning: A Systematic Review. - Enrique Dehaerne; Bappaditya Dey; Sandip Halder; Stefan De Gendt; Wannes Meert (IEEE Access, 2022)

Adversarial Example Detection in Deployed Tree Ensembles. - Laurens Devos; Wannes Meert; Jesse Davis (CoRR, 2022)

DPU: DAG Processing Unit for Irregular Graphs With Precision-Scalable Posit Arithmetic in 28 nm. - Nimish Shah; Laura Isabel Galindez Olascoaga; Shirui Zhao; Wannes Meert; Marian Verhelst (IEEE J. Solid State Circuits, 2022)

GraphOpt: Constrained-Optimization-Based Parallelization of Irregular Graphs. - Nimish Shah; Wannes Meert; Marian Verhelst (IEEE Trans. Parallel Distributed Syst., 2022)

Detecting Evasion Attacks in Deployed Tree Ensembles. - Laurens Devos; Lorenzo Perini; Wannes Meert; Jesse Davis (ECML/PKDD (5), 2023)

Estimating Dynamic Time Warping Distance Between Time Series with Missing Data. - Aras Yurtman; Jonas Soenen; Wannes Meert; Hendrik Blockeel (ECML/PKDD (5), 2023)

A novel reject option applied to sleep stage scoring. - Dries Van der Plas; Wannes Meert; Johan Verbraecken; Jesse Davis (SDM, 2023)

AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection. - Jonas Soenen; Elia Van Wolputte; Vincent Vercruyssen; Wannes Meert; Hendrik Blockeel (CoRR, 2023)

Deriving Comprehensible Theories from Probabilistic Circuits. - Sieben Bocklandt; Wannes Meert; Koen Vanderstraeten; Wouter Pijpops; Kurt Jaspers (CoRR, 2023)

A Machine Learning Approach Towards SKILL Code Autocompletion. - Enrique Dehaerne; Bappaditya Dey; Wannes Meert (CoRR, 2023)

Pattern-based Time Series Semantic Segmentation with Gradual State Transitions. - Louis Carpentier; Len Feremans; Wannes Meert; Mathias Verbeke (SDM, 2024)

Quantitative Evaluation of Motif Sets in Time Series. - Daan Van Wesenbeeck; Aras Yurtman; Wannes Meert; Hendrik Blockeel (CoRR, 2024)

Machine learning with a reject option: a survey. - Kilian Hendrickx; Lorenzo Perini; Dries Van der Plas; Wannes Meert; Jesse Davis (Mach. Learn., 2024)

Time-Shifted Transformers for Driver Identification Using Vehicle Data. - Wim Govers; Aras Yurtman; Turgay Aslandere; Nicole Eikelenberg; Wannes Meert; Jesse Davis (IEEE Trans. Intell. Transp. Syst., 2024)

LoCoMotif: discovering time-warped motifs in time series. - Daan Van Wesenbeeck; Aras Yurtman; Wannes Meert; Hendrik Blockeel (Data Min. Knowl. Discov., 2024)

TSFuse: automated feature construction for multiple time series data. - Arne De Brabandere; Tim Op De Beéck; Kilian Hendrickx; Wannes Meert; Jesse Davis (Mach. Learn., 2024)

Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned. - Jesse Davis; Lotte Bransen; Laurens Devos; Arne Jaspers; Wannes Meert; Pieter Robberechts; Jan Van Haaren; Maaike Van Roy (Mach. Learn., 2024)

Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery. - Aras Yurtman; Daan Van Wesenbeeck; Wannes Meert; Hendrik Blockeel (CoRR, 2025)

dtaianomaly: A Python library for time series anomaly detection. - Louis Carpentier; Nick Seeuws; Wannes Meert; Mathias Verbeke (CoRR, 2025)

Warping and Matching Subsequences Between Time Series. - Simiao Lin; Wannes Meert; Pieter Robberechts; Hendrik Blockeel (CoRR, 2025)

MC2A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration. - Shirui Zhao; Jun Yin; Lingyun Yao; Martin Andraud; Wannes Meert; Marian Verhelst (CoRR, 2025)

AIA: A Customized Multi-Core RISC-V SoC for Discrete Sampling Workloads in 16 nm. - Shirui Zhao; Nimish Shah; Wannes Meert; Marian Verhelst (IEEE J. Solid State Circuits, 2025)