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.
Scalable Affinity Propagation for Massive Datasets
Authors: Hiroaki Shiokawa9639-9646
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,445 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,874 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 | 4 Experimental Evaluation We experimentally evaluated the effectiveness of Scale AP by comparing it with the following AP algorithms. [...] Datasets: We used three real-world datasets summarized in Table 1. [...] Figure 1 shows the running time on the real-world datasets, where DNF indicates that the runtime exceeded 24 hours. |
| Researcher Affiliation | Academia | Hiroaki Shiokawa Center for Computational Sciences, University of Tsukuba, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 Proposed method: Scale AP |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Datasets: We used three real-world datasets summarized in Table 1. All the datasets are published by UCI Machine Learning Repository (Dua and Graff 2017). |
| Dataset Splits | No | The paper mentions using real-world datasets but does not explicitly provide training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments were conducted on a server with Intel Xeon CPU 2.60 GHz and 768 Gi B RAM. |
| Software Dependencies | No | The paper discusses various algorithms and methods but does not provide specific version numbers for software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | The experiments used the negative Euclidean distance as the similarity between data objects. In accordance with (Frey and Dueck 2007), we set the preference to both the median and minimum of the similarities, λ = 0.5, and the maximum number of iterations to T = 1, 000. |