Sebastijan Dumančić | DeepAI

DeepAI

Featured Co-authors

Mathias Niepert
44 publications
Thomas Demeester
36 publications
Luc De Raedt
28 publications
Andrew Cropper
22 publications
Giuseppe Marra
21 publications
Tias Guns
19 publications
Hendrik Blockeel
17 publications
Alberto García-Durán
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Wannes Meert
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Richard Evans
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Angelika Kimmig
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∙ 01/08/2023

A Divide-Align-Conquer Strategy for Program Synthesis

A major bottleneck in search-based program synthesis is the exponentiall...

Jonas Witt, et al. ∙

∙ 12/01/2021

SaDe: Learning Models that Provably Satisfy Domain Constraints

With increasing real world applications of machine learning, models are ...

Kshitij Goyal, et al. ∙

∙ 08/25/2021

From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey

Neural-symbolic and statistical relational artificial intelligence both ...

Giuseppe Marra, et al. ∙

∙ 02/21/2021

Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learn...

Andrew Cropper, et al. ∙

∙ 08/18/2020

Inductive logic programming at 30: a new introduction

Inductive logic programming (ILP) is a form of machine learning. The goa...

Andrew Cropper, et al. ∙

∙ 07/11/2020

Feature Interactions in XGBoost

In this paper, we investigate how feature interactions can be identified...

Kshitij Goyal, et al. ∙

∙ 04/21/2020

Knowledge Refactoring for Program Induction

Humans constantly restructure knowledge to use it more efficiently. Our ...

Sebastijan Dumančić, et al. ∙

∙ 04/21/2020

Learning large logic programs by going beyond entailment

A major challenge in inductive logic programming (ILP) is learning large...

Andrew Cropper, et al. ∙

∙ 03/18/2020

From Statistical Relational to Neuro-Symbolic Artificial Intelligence

Neuro-symbolic and statistical relational artificial intelligence both i...

Luc De Raedt, et al. ∙

∙ 02/25/2020

Turning 30: New Ideas in Inductive Logic Programming

Common criticisms of state-of-the-art machine learning include poor gene...

Andrew Cropper, et al. ∙

∙ 03/29/2019

Learning Relational Representations with Auto-encoding Logic Programs

Deep learning methods capable of handling relational data have prolifera...

Sebastijan Dumančić, et al. ∙

∙ 09/10/2018

Learning Sequence Encoders for Temporal Knowledge Graph Completion

Research on link prediction in knowledge graphs has mainly focused on st...

Alberto García-Durán, et al. ∙

∙ 06/29/2018

On embeddings as an alternative paradigm for relational learning

Many real-world domains can be expressed as graphs and, more generally, ...

Sebastijan Dumančić, et al. ∙

∙ 06/29/2018

On embeddings as alternative paradigm for relational learning

Many real-world domains can be expressed as graphs and, more generally, ...

Sebastijan Dumančić, et al. ∙

∙ 05/28/2018

DeepProbLog: Neural Probabilistic Logic Programming

We introduce DeepProbLog, a probabilistic logic programming language tha...

Robin Manhaeve, et al. ∙

∙ 05/02/2018

COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series

Clustering is ubiquitous in data analysis, including analysis of time se...

Toon Van Craenendonck, et al. ∙

∙ 03/29/2018

COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints

Constraint-based clustering algorithms exploit background knowledge to c...

Toon Van Craenendonck, et al. ∙

∙ 01/30/2018

COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints

Clustering is inherently ill-posed: there often exist multiple valid clu...

Toon Van Craenendonck, et al. ∙

∙ 05/16/2017

Demystifying Relational Latent Representations

Latent features learned by deep learning approaches have proven to be a ...

Sebastijan Dumančić, et al. ∙

∙ 06/28/2016

Theory reconstruction: a representation learning view on predicate invention

With this positional paper we present a representation learning view on ...

Sebastijan Dumančić, et al. ∙

∙ 06/28/2016

Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation

The goal of unsupervised representation learning is to extract a new rep...

Sebastijan Dumančić, et al. ∙

∙ 04/29/2016

An expressive dissimilarity measure for relational clustering using neighbourhood trees

Clustering is an underspecified task: there are no universal criteria fo...

Sebastijan Dumančić, et al. ∙