Online Search with Maximum Clearance
Authors: Spyros Angelopoulos, Malachi Voss3642-3650
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Evaluation In this section we evaluate the performance of our optimal strategy against two other candidate strategies... We used networks obtained from the online library Transportation Network Test Problems (Bar-Gera 2002)... |
| Researcher Affiliation | Academia | Centre National de la Recherche Scientifique (CNRS) Ecole Normale Sup erieure Sorbonne Universit e, Laboratoire d informatique de Paris 6, LIP6 |
| Pseudocode | No | The paper describes methods and formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement about providing open-source code or a link to a code repository for the described methodology was found. |
| Open Datasets | Yes | We used networks obtained from the online library Transportation Network Test Problems (Bar-Gera 2002), after making them undirected. This is a set of benchmarks that is very frequently used in the assessment of transportation network algorithms (see e.g. (Jahn et al. 2005))... Bar-Gera, H. 2002. Transportation Network Test Problems. URL https://github.com/bstabler/Transportation Networks. Accessed on August 15, 2020. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or cloud instances) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the strategies used (e.g., RPT(r) with r=2) and some general setup like random root selection, but lacks specific details such as hyperparameters, learning rates, or optimizer settings typically found in experimental setups. |