no code implementations • 16 Aug 2023 • Céline Hocquette, Sebastijan Dumančić, Andrew Cropper
We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold.
no code implementations • 8 Jan 2023 • Jonas Witt, Stef Rasing, Sebastijan Dumančić, Tias Guns, Claus-Christian Carbon
We show that compositional segmentation can be applied in the programming by examples setting to divide the search for large programs across multiple smaller program synthesis problems.
no code implementations • 25 Aug 2021 • Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, Luc De Raedt
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.
no code implementations • 21 Feb 2021 • Andrew Cropper, Sebastijan Dumančić, Richard Evans, Stephen H. Muggleton
Inductive logic programming (ILP) is a form of logic-based machine learning.
3 code implementations • 18 Aug 2020 • Andrew Cropper, Sebastijan Dumančić
Inductive logic programming (ILP) is a form of machine learning.
no code implementations • 21 Apr 2020 • Andrew Cropper, Sebastijan Dumančić
We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search.
no code implementations • 18 Mar 2020 • Luc De Raedt, Sebastijan Dumančić, Robin Manhaeve, Giuseppe Marra
Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.
no code implementations • 25 Feb 2020 • Andrew Cropper, Sebastijan Dumančić, Stephen H. Muggleton
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data.
no code implementations • NeurIPS 2018 • Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.
3 code implementations • EMNLP 2018 • Alberto García-Durán, Sebastijan Dumančić, Mathias Niepert
In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.
4 code implementations • NeurIPS 2018 • Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.
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 • 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.