111 citations · 169 across the 15 of their papers we have counts for
9 papers · 1 filter
Inductive logic programming at 30
Andrew Cropper, Sebastijan Dumančić, Richard Evans +1
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP t…
Inductive logic programming at 30: a new introduction
Andrew Cropper, Sebastijan Dumančić
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns…
Knowledge Refactoring for Inductive Program Synthesis
Sebastijan Dumancic, Tias Guns, Andrew Cropper
Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introd…
Neural Probabilistic Logic Programming in DeepProbLog
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig +2
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learni…
Learning Sequence Encoders for Temporal Knowledge Graph Completion
Alberto García-Durán, Sebastijan Dumančić, Mathias Niepert
Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entit…
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Sebastijan Dumancic, Alberto Garcia-Duran, Mathias Niepert
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally…
DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig +2
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning tech…
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
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 knowi…
Demystifying Relational Latent Representations
Sebastijan Dumančić, Hendrik Blockeel
Latent features learned by deep learning approaches have proven to be a powerful tool for machine learning. They serve as a data abstraction that makes learning easier by capturing…