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 [1].
What do Graph Neural Networks learn? Insights from Tropical Geometry
Authors: Tuan Anh Pham, Vikas Garg
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is purely theoretical and does not involve any experimental result. |
| Researcher Affiliation | Collaboration | Tuan Anh Pham School of Mathematics University of Edinburgh Edinburgh, United Kingdom EMAIL Vikas Garg Yai Yai Ltd and Aalto University EMAIL |
| Pseudocode | Yes | Algorithm 1 Building Φ(t) 2 |
| Open Source Code | No | Our work is purely theoretical and does not any data and code. |
| Open Datasets | No | Our work is purely theoretical and does not involve any experimental result. |
| Dataset Splits | No | Our work is purely theoretical and does not involve any experimental result. |
| Hardware Specification | No | Our work is purely theoretical and does not involve any experimental result. |
| Software Dependencies | No | Our work is purely theoretical and does not involve any experimental result. |
| Experiment Setup | No | Our work is purely theoretical and does not involve any experimental result. |