1 code implementation • 29 Nov 2023 • Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert, Hendrik Blockeel
All existing methods for TSMD have one or more of the following limitations: they only look for the two most similar occurrences of a pattern; they only look for patterns of a pre-specified, fixed length; they cannot handle variability along the time axis; and they only handle univariate time series.
no code implementations • 22 May 2023 • Jonas Soenen, Elia Van Wolputte, Vincent Vercruyssen, Wannes Meert, Hendrik Blockeel
Moreover, by identifying patterns and conditions in (low-dimensional) subspaces, the anomaly detector can provide simple explanations of why something is considered an anomaly.
no code implementations • 2 Mar 2023 • Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
In this paper, we present an approach to train neural networks which can enforce a wide variety of constraints and guarantee that the constraint is satisfied by all possible predictions.
no code implementations • 4 Oct 2022 • Kshitij Goyal, Wannes Meert, Hendrik Blockeel, Elia Van Wolputte, Koen Vanderstraeten, Wouter Pijpops, Kurt Jaspers
In this work, we represent product concepts using database queries and tackle two learning problems.
1 code implementation • 15 Aug 2022 • Florian Busch, Moritz Kulessa, Eneldo Loza Mencía, Hendrik Blockeel
A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class.
no code implementations • 1 Dec 2021 • Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
In many real world applications of machine learning, models have to meet certain domain-based requirements that can be expressed as constraints (e. g., safety-critical constraints in autonomous driving systems).
no code implementations • 11 Jul 2020 • Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
Further, the improvement in the model structure can also lead to better interpretability.
no code implementations • 11 Sep 2019 • Jonas Schouterden, Jesse Davis, Hendrik Blockeel
Propositionalization is the process of summarizing relational data into a tabular (attribute-value) format.
no code implementations • 29 Mar 2019 • Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel
This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks.
no code implementations • 2 May 2018 • Toon Van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel
This paper studies semi-supervised clustering in the context of time series.
no code implementations • 29 Mar 2018 • Toon Van Craenendonck, Sebastijan Dumančić, Elia Van Wolputte, Hendrik Blockeel
This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not?
no code implementations • 30 Jan 2018 • Toon Van Craenendonck, Sebastijan Dumancic, Hendrik Blockeel
Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce.
no code implementations • 16 May 2017 • Sebastijan Dumančić, Hendrik Blockeel
This work addresses these issues and shows that (1) latent features created by clustering are interpretable and capture interesting properties of data; (2) they identify local regions of instances that match well with the label, which partially explains their benefit; and (3) although the number of latent features generated by this approach is large, often many of them are highly redundant and can be removed without hurting performance much.
no code implementations • 23 Sep 2016 • Toon Van Craenendonck, Hendrik Blockeel
Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process.
no code implementations • 28 Jun 2016 • Sebastijan Dumancic, Hendrik Blockeel
The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier.
no code implementations • 28 Jun 2016 • Sebastijan Dumancic, Wannes Meert, Hendrik Blockeel
With this positional paper we present a representation learning view on predicate invention.
no code implementations • 29 Apr 2016 • Sebastijan Dumancic, Hendrik Blockeel
It is the first measure to incorporate a wide variety of types of similarity, including similarity of attributes, similarity of relational context, and proximity in a hypergraph.
no code implementations • 4 Feb 2014 • Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel
The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language.
no code implementations • 26 Sep 2013 • Maurice Bruynooghe, Hendrik Blockeel, Bart Bogaerts, Broes De Cat, Stef De Pooter, Joachim Jansen, Anthony Labarre, Jan Ramon, Marc Denecker, Sicco Verwer
This paper provides a gentle introduction to problem solving with the IDP3 system.
no code implementations • NeurIPS 2013 • Nima Taghipour, Jesse Davis, Hendrik Blockeel
Lifting attempts to speed up probabilistic inference by exploiting symmetries in the model.