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.
A Non-parametric Graph Clustering Framework for Multi-View Data
Authors: Shengju Yu, Siwei Wang, Zhibin Dong, Wenxuan Tu, Suyuan Liu, Zhao Lv, Pan Li, Miao Wang, En Zhu
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 21,777 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,074 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 | Numerous experiments reveal Np GC s strong points compared to lots of classical approaches. |
| Researcher Affiliation | Academia | 1School of Computer, National University of Defense Technology, Changsha, 410073, China 2Intelligent Game and Decision Lab, Beijing, 100071, China |
| Pseudocode | Yes | Algorithm 1: Solution to the problem (2) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code. |
| Open Datasets | Yes | All experiments are conducted on the following popular datasets, with sample sizes ranging from 512 to 126054: 1. Calte101view3 1: This dataset... 1https://www.vision.caltech.edu/datasets/ |
| Dataset Splits | No | The paper uses standard public datasets but does not explicitly state the specific training/validation/test splits used for these datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the algorithm and its solution steps but does not provide specific experimental setup details such as hyperparameter values, learning rates, or batch sizes. |