Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures
Authors: Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezny, Steven Schockaert, Ondrej Kuzelka
JAIR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we describe experiments performed on 78 datasets about organic molecules: the Mutagenesis dataset (Lodhi & Muggleton, 2005), four datasets from the predictive toxicology challenge, and 73 NCI datasets (Ralaivola, Swamidass, Saigo, & Baldi, 2005). We compare the performance of LRNNs with the state-of-the-art relational learners k FOIL (Landwehr, Passerini, De Raedt, & Frasconi, 2006) and n FOIL (Landwehr, Kersting, & Raedt, 2007), which respectively combine relational rule learning with support vector machines and with naive Bayes learning. |
| Researcher Affiliation | Academia | Gustav ˇSourek EMAIL Faculty of Electrical Engineering Czech Technical University in Prague Prague, Czech Republic Vojtˇech Aschenbrenner EMAIL Faculty of Mathematics and Physics Charles University Prague, Czech Republic Filip ˇZelezn y EMAIL Faculty of Electrical Engineering Czech Technical University in Prague Prague, Czech Republic Steven Schockaert EMAIL School of Computer Science & Informatics CardiffUniversity Cardiff, United Kingdom Ondˇrej Kuˇzelka EMAIL Department of Computer Science KU Leuven Leuven, Belgium |
| Pseudocode | No | The paper describes the weight learning algorithm textually in Section 3.4 'Weight Learning' but does not provide a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In this section, we describe experiments performed on 78 datasets about organic molecules: the Mutagenesis dataset (Lodhi & Muggleton, 2005), four datasets from the predictive toxicology challenge, and 73 NCI datasets (Ralaivola, Swamidass, Saigo, & Baldi, 2005). |
| Dataset Splits | Yes | Figure 5: Prediction errors of LRNNs, k FOIL, n FOIL, MLN-boost and RDN-boost measured by cross-validation on 78 datasets about organic molecules. |
| Hardware Specification | No | The time for training an LRNN on a standard commodity machine with one CPU was in the order of a few hours for the larger NCI-GI datasets, and in the order of a few minutes for the smaller datasets such as Mutagenesis. |
| Software Dependencies | No | The paper mentions various methods and frameworks (e.g., backpropagation, stochastic gradient descent, MLNs, Problog, CILP++, k FOIL, n FOIL, MLN-boost, RDN-boost, Aleph), but does not provide specific version numbers for the software dependencies used in their implementation of LRNNs. |
| Experiment Setup | Yes | For all the reported experiments, we set the learning rate to 0.3 and training epochs to 3000. |