On the Synergy of Network Science and Artificial Intelligence
Authors: Decebal Constantin Mocanu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At the same time, the practical applicability of these concepts was not let behind, and we have demonstrated their validity in the context of real-world settings, e.g. image/video quality assessment in communication networks [Mocanu et al., 2014b; 2015b; 2015c], computer vision [Mocanu et al., 2014c; 2015a; 2016a]. |
| Researcher Affiliation | Academia | Decebal Constantin Mocanu Eindhoven University of Technology, The Netherlands |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to repositories. |
| Open Datasets | No | The paper mentions 'real-world settings' and cites other papers (e.g., 'image/video quality assessment in communication networks [Mocanu et al., 2014b; 2015b; 2015c]'), but it does not provide concrete access information (link, DOI, repository, or direct citation within this paper) for a publicly available dataset used for its experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |