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 [1].
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. |