Expanding the Hyperbolic Kernels: A Curvature-aware Isometric Embedding View
Authors: Meimei Yang, Pengfei Fang, Hui Xue
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct thorough experiments to evaluate the superiority of the proposed kernels. Source code and Appendix are available at https://github.com/MMeiYang/Code-and-Appendix-for-Expanding-the-Hyperbolic-Kernels-ACurvature-aware-Isometric-Embedding-View. ... Table 1: Mean accuracy (%) of node classification on graph datasets including Facebook, Terrorist, Wiki, AC and Cora ML. ... Table 2: Zero-shot recognition results (%) on the CUB, AWA1, AWA2 datasets. |
| Researcher Affiliation | Academia | Meimei Yang1,2 , Pangfei Fang1,2 , Hui Xue1,2 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2MOE Key Laboratory of Computer Network and Information Integration (Southeast University), China {meimeiyang,fangpengfei,hxue}@seu.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code and Appendix are available at https://github.com/MMeiYang/Code-and-Appendix-for-Expanding-the-Hyperbolic-Kernels-ACurvature-aware-Isometric-Embedding-View. |
| Open Datasets | Yes | Five real-world graph datasets including Facebook [Rozemberczki et al., 2019], Terrorist [Zhao et al., 2006], Wiki [Cucerzan, 2007], Amazon Electronics Computers (AC) [Shchur et al., 2018], Cora ML [Bojchevski and G unnemann, 2017] are used in this study. ... We use CUB [Wah et al., 2011], AWA1 [Lampert et al., 2013] and AWA2 [Akata et al., 2016] to evaluate the ZSL task. |
| Dataset Splits | No | The paper mentions a 'hold-out data' and an 'one-vs-all (OVA) strategy' but does not provide specific details on how the dataset was split into training, validation, and test sets, or if k-fold cross-validation was used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for libraries or frameworks used in the experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings used during training. |