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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Logical characterizations of recurrent graph neural networks with reals and floats
Authors: Veeti Ahvonen, Damian Heiman, Antti Kuusisto, Carsten Lutz
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this article, we give exact logical characterizations of recurrent GNNs in two scenarios: (1) in the setting with floating-point numbers and (2) with reals. This is a theoretical paper and all results listed in the abstract and intro are ultimately justified via proofs, occasionally with proof sketches with full proofs in the appendix. |
| Researcher Affiliation | Academia | 1Tampere University, 2Leipzig University, 3Sca DS.AI, Dresden/Leipzig EMAIL, 2,EMAIL |
| Pseudocode | No | The paper describes theoretical concepts and proofs but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not describe a methodology that involves open-source code for its implementation. The NeurIPS checklist confirms: 'The paper does not include experiments requiring code.' |
| Open Datasets | No | The paper is purely theoretical and does not involve the use of any datasets for training or evaluation. |
| Dataset Splits | No | The paper is purely theoretical and does not involve experimental data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not include experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not include experimental setup details such as hyperparameters or training configurations. |