Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Common-Individual Semantic Fusion for Multi-View Multi-Label Learning
Authors: Gengyu Lyu, Weiqi Kang, Haobo Wang, Zheng Li, Zhen Yang, Songhe Feng
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 19,693 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,493 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various data sets have verified the superiority of our method. (Abstract) and To evaluate the performance of our proposed CISF method, we implement experiments on seven widely-used MVML data sets, including Emotions, Scene, Corel5k, Espgame, Pascal, Iaprtc12 and Mirflickr data sets. (Section 4.1) |
| Researcher Affiliation | Academia | Faculty of Information Technology, Beijing University of Technology, School of Software Technology, Zhejiang University, School of Computer Science and Technology, Beijing Jiaotong University |
| Pseudocode | Yes | Algorithm 1 The Training Process of CISF |
| Open Source Code | Yes | The codes and data sets are provided in https://gengyulyu.github.io/homepage/. |
| Open Datasets | Yes | To evaluate the performance of our proposed CISF method, we implement experiments on seven widely-used MVML data sets, including Emotions, Scene, Corel5k, Espgame, Pascal, Iaprtc12 and Mirflickr data sets. (Section 4.1) and The codes and data sets are provided in https://gengyulyu.github.io/homepage/. (Section 4.1) |
| Dataset Splits | Yes | For each dataset, we randomly select 70% examples for training, 10% examples for parameter tuning and 20% examples for evaluation, where each algorithm is run 5 times independently. (Section 4.1) |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | The parameter analysis of CISF with respect to its four employed parameters α, β, γ and η. ... we select the optimal values of them from {10 3, 10 2, . . . , 102} and {0.01, 0.05, . . . , 10}, respectively. Meanwhile, other parameters often follow the optimal configurations β = 0.1 and η = 100 but vary with minor adjustments on different data sets. In addition, in our experiments, the value of λmax is set to 1e6 and the maximum iterations Imax is set to 50. (Section 5.2) |