Toward Interactive Relational Learning
Authors: Ryan Rossi, Rong Zhou
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An overview of our i RML system is shown in Figure 2. In that example, we first interactively learn a model, then select the misclassified nodes for further analysis. ... This screenshot is from cora a common RML benchmark data set (Macskassy et al.). |
| Researcher Affiliation | Industry | Ryan Rossi and Rong Zhou Palo Alto Research Center |
| Pseudocode | No | The paper describes the system's functionalities and components but does not include 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 a code repository. |
| Open Datasets | Yes | This screenshot is from cora a common RML benchmark data set (Macskassy et al.). |
| Dataset Splits | No | The paper mentions the use of the 'cora' dataset but does not provide specific details regarding training, validation, or test dataset splits, percentages, or a splitting methodology. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or cloud infrastructure) used for its implementation or any implied experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as programming languages, libraries, or frameworks with their version numbers. |
| Experiment Setup | No | The paper describes the types of components and parameters that users can interactively specify within the iRML system (e.g., kernel function, hyper-parameters, normalization scheme), but it does not provide the specific values of these parameters or other detailed experimental setup configurations used for any presented examples or implied evaluations. |