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..
Multi-instance multi-label active learning
Authors: Sheng-Jun Huang, Nengneng Gao, Songcan Chen
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark datasets demonstrate that the proposed approach achieves superior performance on various criteria. |
| Researcher Affiliation | Academia | Sheng-Jun Huang and Nengneng Gao and Songcan Chen College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization EMAIL |
| Pseudocode | Yes | Algorithm 1 The MIML-AL Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | Among the public available MIML datasets, there are four datasets, i.e., MSRC [Winn et al., 2005], Letter Frost, Letter Carroll and Bird Song [Briggs et al., 2012] where the labels of instances are available. |
| Dataset Splits | No | The paper mentions random sampling of 20% of bags as test data and 5% for initial labeling, but does not explicitly describe a separate validation set or split for hyperparameter tuning or early stopping during training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For MIML-AL, we fix the parameters b = 200, C = 10 for all datasets. |