Complete Neural Networks for Complete Euclidean Graphs
Authors: Snir Hordan, Tal Amir, Steven J. Gortler, Nadav Dym
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We present synthetic experiments that demonstrate that 2-SEWL can separate challenging pointcloud pairs that cannot be separated by several popular architectures. We trained the architectures on permuted and rotated variations of highly-challenging point-cloud pairs, and measured separation by the test classification accuracy. ... The results of this experiment are given in Table 1. |
| Researcher Affiliation | Academia | 1 Faculty of Mathematics, Technion Israel Institute of Technology, Haifa, Israel 2School of Engineering and Applied Sciences, Harvard University, Cambridge, USA 3 Faculty of Computer Science, Technion Israel Institute of Technology, Haifa, Israel |
| Pseudocode | No | The paper does not contain any explicit 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper mentions 'GNN implementations and code pipeline based on (Joshi et al. 2022)' but does not provide a link or explicit statement about the availability of their own source code for the methodology described. |
| Open Datasets | Yes | We considered three pairs of point clouds (Hard1-Hard3) from Pozdnyakov et al. (2020). |
| Dataset Splits | No | The paper states 'We trained the architectures on permuted and rotated variations of highly-challenging point-cloud pairs, and measured separation by the test classification accuracy.' but does not provide specific training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper states 'GNN implementations and code pipeline based on (Joshi et al. 2022)' but does not provide specific software names with version numbers. |
| Experiment Setup | No | The paper states 'Further details on the experimental setup appear in Appendix B.' but does not provide specific experimental setup details or hyperparameter values in the main text. |