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.
Revealing Common Statistical Behaviors in Heterogeneous Populations
Authors: Andrey Zhitnikov, Rotem Mulayoff, Tomer Michaeli
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,564 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,894 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 illustrate the effectiveness of our methods through extensive simulations as well as on real-data from f MRI scans and from arterial blood pressure and photoplethysmogram measurements. |
| Researcher Affiliation | Academia | Electrical Engineering Dept., Technion, Israel. |
| Pseudocode | Yes | Algorithm 1 Common covariance estimation. ... Algorithm 2 Common density estimation. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the methodology is open-sourced or publicly available. |
| Open Datasets | Yes | ADHD200-preprocessed dataset (Bellec et al., 2017). ... MIMIC 2 dataset (Kachuee et al., 2015). |
| Dataset Splits | No | The paper uses datasets but does not explicitly provide details on train/validation/test splits, specific percentages, or sample counts for reproducing data partitioning. |
| Hardware Specification | Yes | The running time of Alg. 1 was about 10s on an 8 core Intel i7-6700 with 16GB of RAM working at 3.40GHz. |
| Software Dependencies | No | We used the nilearn and scikit-learn python packages (Abraham et al., 2014; Pedregosa et al., 2011; Buitinck et al., 2013). |
| Experiment Setup | Yes | In particular, we used preprocessed resting state f MRI data... We removed nuisance variance... applied a temporal bandpass ๏ฌlter (0.009 Hz < f < 0.08 Hz) ... and a spatial Gaussian ๏ฌlter (6mm FWHM). ... For each subject, we then estimated the 2D pdf of ABP and PPG using Gaussian KDE with bandwidth 0.08. |