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..
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation
Authors: Sebastijan Dumancic, Hendrik Blockeel
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate the proposed approach and show that models learned on such latent representations perform better, have lower complexity, and outperform the existing approaches on classification tasks. |
| Researcher Affiliation | Academia | Sebastijan Dumanˇci c and Hendrik Blockeel Computer Science Department, KU Leuven, Belgium EMAIL |
| Pseudocode | No | The paper describes the CUR2LED procedure conceptually and with an illustration in Figure 1, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about the release of source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper lists datasets like IMDB, UW-CSE, Mutagenesis, Web KB, Terrorists, and Hepatitis, but does not provide specific access information (e.g., URL, DOI, repository, or formal citation including authors and year for dataset retrieval). |
| Dataset Splits | Yes | In order to do so, we use TILDE [Blockeel and De Raedt, 1998], a relational decision tree learner, and perform 5-fold cross validation. |
| Hardware Specification | No | The paper mentions that 'a single CPU' was used for certain experiments and that the approach is 'easily parallelizable' potentially on a 'GPU', but it does not specify concrete hardware models (e.g., CPU or GPU models, memory details) used for the experiments. |
| Software Dependencies | No | The paper mentions using 'TILDE [Blockeel and De Raedt, 1998]', but does not provide specific version numbers for this or any other software dependencies like libraries or frameworks. |
| Experiment Setup | Yes | The following similarity interpretation were used for each dataset: (0.5,0.5,0.0,0.0,0.0), (0.0,0.0,0.33,0.33,0.34), (0.2,0.2,0.2,0.2,0.2). ... We use the following values for the α parameter in Equation 1: {0.1, 0.05, 0.01}. In the case of MRC, we used the following values for the λ parameter: { 1, 5, 10}. In the experiments we use spectral and hierarchical clustering. |