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A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning.

, , and . IJCAI, page 6088-6094. ijcai.org, (2019)

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avatar - Automated Feature Wrangling for Machine Learning., , , and . IDA, volume 12695 of Lecture Notes in Computer Science, page 235-247. Springer, (2021)DeepProbLog: Neural Probabilistic Logic Programming., , , , and . BNAIC/BENELEARN, volume 2491 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)Tackling Noise in Active Semi-supervised Clustering., , , and . ECML/PKDD (2), volume 12458 of Lecture Notes in Computer Science, page 121-136. Springer, (2020)SaDe: Learning Models that Provably Satisfy Domain Constraints., , and . ECML/PKDD (5), volume 13717 of Lecture Notes in Computer Science, page 410-425. Springer, (2022)Neuro-Symbolic = Neural + Logical + Probabilistic., , , , and . NeSy@IJCAI, (2019)Inductive Logic Programming At 30: A New Introduction., and . J. Artif. Intell. Res., (2022)Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation., and . IJCAI, page 1631-1637. ijcai.org, (2017)COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints., , , and . CoRR, (2018)Learning Large Logic Programs By Going Beyond Entailment., and . IJCAI, page 2073-2079. ijcai.org, (2020)Scheduled for July 2020, Yokohama, Japan, postponed due to the Corona pandemic..Turning 30: New Ideas in Inductive Logic Programming., , and . IJCAI, page 4833-4839. ijcai.org, (2020)Survey track.