Random Laplacian Features for Learning with Hyperbolic Space
Authors: Tao Yu, Christopher De Sa
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, we evaluate our approach empirically. Our Hy La-networks demonstrate better performance, scalability and computation speed than existing hyperbolic networks: Hy La-networks consistently outperform HGCN, even on a tree dataset, with 12.3% improvement while being 4.4 faster. |
| Researcher Affiliation | Academia | Anonymous authors Paper under double-blind review |
| Pseudocode | Yes | Algorithm 1 End-to-End Hy La |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of its own source code. It mentions 'publicly released version' for baselines like HGCN, but not for Hy La. |
| Open Datasets | Yes | We use transductive datasets: Cora, Citeseer and Pubmed (Sen et al., 2008), which are standard citation networks benchmarks, following the standard splits adopted in Kipf & Welling (2016). |
| Dataset Splits | Yes | We follow the standard splits Kipf & Welling (2016) with 20 nodes per class for training, 500 nodes for validation and 1000 nodes for test. |
| Hardware Specification | Yes | We measure the training time on a NVIDIA Ge Force RTX 2080 Ti GPU and show the specific timing statistics in Appendix. |
| Software Dependencies | No | The paper mentions optimizers like 'Riemannian SGD optimizer Bonnabel (2013)' and 'Adam Kingma & Ba (2014) optimizer' but does not specify software versions for any libraries or frameworks used in the implementation. |
| Experiment Setup | Yes | We provide the detailed values of hyper-parameters for node classification and text classification in Table 5 and Table 6 respectively. |