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..
Instance-based Learning for Knowledge Base Completion
Authors: Wanyun Cui, Xingran Chen
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various tasks confirmed the IBL model s effectiveness and interpretability. |
| Researcher Affiliation | Academia | Shanghai University of Finance and Economics1 University of Michigan2 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1We release code at https://github.com/chenxran/Instance Based Learning |
| Open Datasets | Yes | Datasets: We select four typical KBC datasets for evaluation, including FB15k-237, WN18RR, Kinship, and UMLS 2. For Kinship and UMLS, we use the training/validation/test division in [17]. |
| Dataset Splits | Yes | For Kinship and UMLS, we use the training/validation/test division in [17]. |
| Hardware Specification | Yes | All experiments can be run on a single Nvidia Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions training techniques and refers to previous work but does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states 'We illustrate the hyper-parameter search process in the Appendix,' which defers specific experimental setup details to supplementary material not included in the main text. |