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..
Learning From Multi-Dimensional Partial Labels
Authors: Haobo Wang, Weiwei Liu, Yang Zhao, Tianlei Hu, Ke Chen, Gang Chen
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on both synthetic and realworld datasets validate the effectiveness of our proposals. |
| Researcher Affiliation | Academia | 1Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University 2College of Computer Science and Technology, Zhejiang University 3School of Computer Science, Wuhan University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The MDC datasets are collected from UCI repository [Dheeru and Karra Taniskidou, 2017]: 1) Bridges estimates bridge properties from specific constraints; 2) WQplant and WQanimals determine the plant and animal species in Slovenian rivers; 3) Flare predicts number of times that certain types of solar flare occurred within 24 hours period; 4) Thyroid determines types of thyroid problems based on patient information. |
| Dataset Splits | No | The paper states, 'All the datasets are randomly split in to 80% training and 20% testing.' However, it does not explicitly mention a separate validation split. |
| Hardware Specification | Yes | All the computations are performed on a workstation with an i7-5930K CPU, a TITAN Xp GPU and 64GB main memory running Linux platform. |
| Software Dependencies | No | The paper mentions models like VGG-19 and methods like CLPL, but it does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | The number of nearest neighbors is set as k = 10 for all the k NN-based approaches. Following the experimental setting in [Fang and Zhang, 2019], we set thr = 0.9 and α = 0.95 for P-VLS. Finally, following [Shen et al., 2018], the parameters of Co H are set as α = 100 and d = 10. |