Titlebook: Inductive Logic Programming; 32nd International C Elena Bellodi,Francesca Alessandra Lisi,Riccardo Z Conference proceedings 2023 The Editor

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,Regularization in?Probabilistic Inductive Logic Programming,erform inference in a lifted way. LIFTCOVER is an algorithm used to perform parameter and structure learning of liftable probabilistic logic programs. In particular, it performs parameter learning via Expectation Maximization and LBFGS. In this paper, we present an updated version of LIFTCOVER, call

Anticlimax

Towards ILP-Based , Passive Learning,inatorial nature of the problem, current state-of-the-art solutions are based on exhaustive search. They use an example at the time to discard a single candidate formula at the time, instead of exploiting the full set of examples to prune the search space. This hinders their applicability when examp

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BLANC

,Select First, Transfer Later: Choosing Proper Datasets for?Statistical Relational Transfer Learningtional and rich probability structures. Although SRL techniques have succeeded in many real-world applications, they follow the same assumption as most ML techniques by assuming training and testing data have the same distribution and are sampled from the same feature space. Changes between these di

被告

,GNN Based Extraction of?Minimal Unsatisfiable Subsets,atisfiability which, as a result, have been used in various applications. Although various systematic algorithms for the extraction of MUSes have been proposed, few heuristic methods have been studied, as the process of designing efficient heuristics requires extensive experience and expertise. In t

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,An Experimental Overview of?Neural-Symbolic Systems,, more and more researchers have encountered the limitations of deep learning, which has led to a rise in the popularity of neural-symbolic AI, with a wide variety of systems being developed. However, many of these systems are either evaluated on different benchmarks, or introduce new benchmarks tha

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