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..
Deep Graph Matching for Partial Label Learning
Authors: Gengyu Lyu, Yanan Wu, Songhe Feng
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various data sets have demonstrated the superiority of our proposed method. |
| Researcher Affiliation | Academia | School of Computer and Information Technology, Beijing Jiaotong University EMAIL |
| Pseudocode | No | The paper includes diagrams (e.g., Figure 1) to illustrate the architecture but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include a statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We implement experiments on four UCI data sets and six realworld data sets: (1) Synthetic data sets. Under different configurations of two controlling parameters (i.e. p and r), four UCI data sets generate 84 (7 3 4) synthetic data sets [Cour et al., 2011] |
| Dataset Splits | Yes | We adopt tenfold cross-validation to train the model and record the experimental results in Figure 2, Table 2 and 3. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions, or solver versions). |
| Experiment Setup | Yes | We study the sensitivity of D-GAP with respect to its two parameters: k and L. Figure 3(a)-(b) shows the performance of D-GAP under different parameter configurations on Lost data set. ... We set k among {3, 5, 8, 10, 15, 20} and L among {3, 5, 10, 20, 30} via cross-validation. |