Learning Deep Generative Models for Queuing Systems
Authors: Cesar Ojeda, Kostadin Cvejoski, Bodgan Georgiev, Christian Bauckhage, Jannis Schuecker, Ramses J. Sanchez9214-9222
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 speciļ¬cation can be found in the Supplementary Material. (Not in main text) |