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..
Partial Multi-Label Learning with Label Distribution
Authors: Ning Xu, Yun-Peng Liu, Xin Geng6510-6517
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic as well as real-world datasets clearly validate the effectiveness of PML-LD for solving PML problems. |
| Researcher Affiliation | Academia | Ning Xu, Yun-Peng Liu, Xin Geng MOE Key Laboratory of Computer Network and Information Integration, China School of Computer Science and Engineering, Southeast University, Nanjing 210096, China EMAIL |
| Pseudocode | No | The paper describes methods in textual paragraphs but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of its source code or links to a code repository. |
| Open Datasets | Yes | five benchmark multi-label datasets (Zhang and Zhou 2014) are used to generate synthetic PML datasets, including image, emotions, scene, yeast, and eurlex sm. Furthermore, three real-world PML datasets including music emotion, music style and mirflickr (Huiskes and Lew 2008) are also employed in this paper. |
| Dataset Splits | Yes | On each dataset, five-fold cross-validation is performed where the mean metric value as well as standard deviation are recorded for each comparing approach. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | For PML-LD, the parameter λ1, λ2, m, β1 and β2 are fix to 0.01, 0.01, 20, 1, 10 respectively. The kernel function in PML-LD is Gaussian kernel. |