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].
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. |