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..
ER: Equivariance Regularizer for Knowledge Graph Completion
Authors: Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Qingming Huang5512-5520
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first introduce the experimental settings and show main results. Then we conduct some ablation experiments. |
| Researcher Affiliation | Academia | Zongsheng Cao 1,2, Qianqian Xu 3,*, Zhiyong Yang 4, Qingming Huang 3,4,5,6,* 1 State Key Laboratory of Information Security, Institute of Information Engineering, CAS, Beijing, China 2 School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China 3 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, CAS, Beijing, China 4 School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China 5 Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, China 6 Peng Cheng Laboratory, Shenzhen, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Appendix1. 1https://github.com/Lion-ZS/ER |
| Open Datasets | Yes | We conduct experiments on three widely used benchmarks, WN18RR (Dettmers et al. 2017), FB15K-237 (Dettmers et al. 2017) and YAGO3-10 (Mahdisoltani, Biega, and Suchanek 2013) |
| Dataset Splits | Yes | We take Adagrad (Duchi, Hazan, and Singer 2011) as the optimizer in the experiment, and use grid search based on the performance of the validation datasets to choose the best hyperparameters. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions 'Adagrad' as an optimizer but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Specifically, we search learning rates in {0.5, 0.1, 0.05, 0.01, 0.005, 0.001}, and search regularization coefficients in {0.001, 0.005, 0.01, 0.05, 0.1, 0.5}. All models are trained for a maximum of 200 epochs. |