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..
Learning Deep Generative Models for Queuing Systems
Authors: Cesar Ojeda, Kostadin Cvejoski, Bodgan Georgiev, Christian Bauckhage, Jannis Schuecker, Ramses J. Sanchez9214-9222
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive experimental analysis on both empirical and synthetic datasets, showing the effectiveness of the proposed models. |
| Researcher Affiliation | Collaboration | 1Berlin Center for Machine Learning and TU Berlin, 10587 Berlin, Germany 2Competence Center Machine Learning Rhine-Ruhr 3Fraunhofer Center for Machine Learning and Fraunhofer IAIS, 53757 Sankt Augustin, Germany 4B-IT, University of Bonn, Bonn, Germany 5Bayer AG, Germany |
| Pseudocode | Yes | Algorithm 1: Recurrent Adversarial Service Time |
| Open Source Code | No | The paper does not provide a direct link to source code or an explicit statement that the code for the described methodology is publicly available. |
| Open Datasets | Yes | New York City Taxi Dataset (NY): ...We analyse about 1.1 10^7 trips. https://www1.nyc.gov/site/tlc/about/data-and-research.page |
| Dataset Splits | No | The paper states 'We split all datasets into training and test sets', but does not explicitly mention or detail a validation set split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | No | Details of the neural networks architectures, learning parameters and any other hyperparameters as required in the model specification can be found in the Supplementary Material. (Not in main text) |