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.
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
Authors: Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,387 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,540 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 empirically illustrate our metric with an example of rotation dynamics in a unit disk in a complex plane, and evaluate the performance with real-world time-series data. |
| Researcher Affiliation | Collaboration | RIKEN Center for Advanced Intelligence Project School of Fundamental Science and Technology, Keio University The Institute of Scientific and Industrial Research, Osaka University EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods mathematically and in prose, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The Matlab code is available at https://github.com/keisuke198619/metric NLDS |
| Open Datasets | Yes | We used the UCR time series classification archive as open-source real-world data [5]. |
| Dataset Splits | Yes | For clear visualization, we randomly selected 20 sequences for each label from validation data... We used 40 sequences for each label and computed averaged 10-fold cross-validation error (over 10 random trials). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab code' but does not specify the version of Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For the Gaussian kernel, the kernel width was set as the median of the distances from data. ... We used k-nearest neighbor classifier (k = 3) for simplicity. |