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..
Colour Passing Revisited: Lifted Model Construction with Commutative Factors
Authors: Malte Luttermann, Tanya Braun, Ralf Möller, Marcel Gehrke
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we provide experiments confirming that ACP yields significantly faster inference times compared to the state of the art. |
| Researcher Affiliation | Academia | 1German Research Center for Artificial Intelligence (DFKI), Lübeck, Germany 2Institute of Information Systems, University of Lübeck, Germany 3Data Science Group, University of Münster, Germany |
| Pseudocode | Yes | Algorithm 1 presents the entire ACP algorithm, which is explained in more detail in the following. |
| Open Source Code | Yes | We provide the data set generators along with our source code in the supplementary material. |
| Open Datasets | No | The paper states 'We provide the data set generators along with our source code in the supplementary material' and describes how datasets are generated, but it does not specify a concrete, publicly available dataset with a link or formal citation. |
| Dataset Splits | No | The paper evaluates query times on generated FGs and does not describe traditional training, validation, or test dataset splits for a machine learning model. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments, only general references to 'query times'. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for the experiments. |
| Experiment Setup | No | The paper does not provide specific details about the experimental setup, such as hyperparameter values (e.g., learning rate, batch size) or other system-level training settings. |