Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation
Authors: Sebastijan Dumancic, Hendrik Blockeel
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 {sebastijan.dumancic,hendrik.blockeel}@cs.kuleuven.be |
| 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. |