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.
Bayesian Dynamic Mode Decomposition
Authors: Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,397 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,120 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 | We investigate the empirical performance of Bayesian DMD using synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Department of Aeronautics and Astronautics, The University of Tokyo The Institute of Scientific and Industrial Research, Osaka University RIKEN Center for Advanced Intelligence Project |
| Pseudocode | Yes | Algorithm 1 (DMD [Tu et al., 2014]). |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology described in this paper is found. |
| Open Datasets | Yes | We chose locomotion data of three subjects (Subjects #2, #16 and #35), for which both walk and run/jog motions were recorded.1 Downloaded from http://mocap.cs.cmu.edu/. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits (e.g., percentages, sample counts, or cross-validation details) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud resources) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In our implementation, the hyperparameters α and β were set to 10-3. |