Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions
Authors: Hideaki Kim
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 22,300 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,926 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 con๏ฌrm the effectiveness of our results by experiments on the problems of non-negativity-constrained regression, density estimation, and intensity estimation. |
| Researcher Affiliation | Industry | Hideaki Kim NTT Corporation EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data to reproduce the results are available at https://github.com/Hid Kim/IM-Kernel. |
| Open Datasets | Yes | Code and data to reproduce the results are available at https://github.com/Hid Kim/IM-Kernel. |
| Dataset Splits | Yes | The hyper-parameters for each model were optimized through three-fold cross validation on a grid |
| Hardware Specification | Yes | A Mac Book Pro with 12-core CPU (Apple M2 Max) was used. |
| Software Dependencies | Yes | We implemented all compared models by using Python-3.10.8 (Sci Py-1.11, fnnls-1.0 (MIT License))1. |
| Experiment Setup | Yes | The hyper-parameters for each model were optimized through three-fold cross validation on a grid: for NCM, QNM, and IMK, the grid is (ฯ, r) C C for C = {0.1, 0.2, 0.5, 1, 2, 5, 10}; for SNF, the number of components for Gaussian mixture measure dยต( ) was selected from {1, 2, 3}. |