863 citations · 1.1k across the 19 of their papers we have counts for
11 papers · 1 filter
Warping and Matching Subsequences Between Time Series
Simiao Lin, Wannes Meert, Pieter Robberechts +1
Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparis…
Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery
Aras Yurtman, Daan Van Wesenbeeck, Wannes Meert +1
Time Series Motif Discovery (TSMD) identifies repeating patterns in time series data, but its unsupervised nature might result in motifs that are not interesting to the user. To ad…
Quantitative Evaluation of Motif Sets in Time Series
Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert +1
Time Series Motif Discovery (TSMD), which aims at finding recurring patterns in time series, is an important task in numerous application domains, and many methods for this task ex…
LoCoMotif: Discovering time-warped motifs in time series
Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert +1
Time Series Motif Discovery (TSMD) refers to the task of identifying patterns that occur multiple times (possibly with minor variations) in a time series. All existing methods for…
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming
Kshitij Goyal, Wannes Meert, Hendrik Blockeel +4
Internet based businesses and products (e.g. e-commerce, music streaming) are becoming more and more sophisticated every day with a lot of focus on improving customer satisfaction.…
Elastic Product Quantization for Time Series
Pieter Robberechts, Wannes Meert, Jesse Davis
Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements. Therefore, techniques have been proposed to generate c…
Machine Learning with a Reject Option: A survey
Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas +2
Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can…
Versatile Verification of Tree Ensembles
Laurens Devos, Wannes Meert, Jesse Davis
Machine learned models often must abide by certain requirements (e.g., fairness or legal). This has spurred interested in developing approaches that can provably verify whether a m…
A general anomaly detection framework for fleet-based condition monitoring of machines
Kilian Hendrickx, Wannes Meert, Yves Mollet +4
Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial…
Verifying Tree Ensembles by Reasoning about Potential Instances
Laurens Devos, Wannes Meert, Jesse Davis
Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?", "Does a specific attribute have a disproportionate effect on the model's predi…
Learning Relational Representations with Auto-encoding Logic Programs
Sebastijan Dumancic, Tias Guns, Wannes Meert +1
Deep learning methods capable of handling relational data have proliferated over the last years. In contrast to traditional relational learning methods that leverage first-order lo…