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 [1].
Multiparameter Persistence Image for Topological Machine Learning
Authors: Mathieu Carrière, Andrew Blumberg
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we demonstrate its efficacy by comparing its performance to other multiparameter descriptors on several classification tasks. |
| Researcher Affiliation | Academia | Mathieu Carrière Data Shape Inria Sophia-Antipolis Biot, France EMAIL Andrew J. Blumberg Department of Mathematics University of Texas at Austin Austin, TX 78712 EMAIL |
| Pseudocode | No | The paper describes algorithms but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | We also provide open-source implementation for our descriptor and for the other approaches [CFK+19, Vip20], in a public Python package [Car20]. |
| Open Datasets | Yes | We use time series data sets from the UCR archive [DBK+18] with moderate sizes and lengths; this ensures that the kernel matrices obtained with Multiparameter Persistence Kernel have reasonable sizes and that the point clouds obtained with the Takens embedding in R3 have a reasonable number of points. |
| Dataset Splits | Yes | Moreover, we use the train/test split that is suggested for each data set. Resolutions for Multiparameter Persistence Landscapes and Images are 5-fold cross-validated over the set of values {10, 50}, and the powers and bandwidths of the Multiparameter Persistence Images and Kernels are also 5-fold cross-validated with values in {0, 1} (power) and 10{ 2, 1,0,1,2} (bandwidth). |
| Hardware Specification | Yes | All experiments have been run on an AWS machine with a Xeon Platinum 8175 processor. |
| Software Dependencies | No | The paper mentions a "public Python package" but does not list specific software dependencies with version numbers (e.g., Python version, library versions). |
| Experiment Setup | Yes | Resolutions for Multiparameter Persistence Landscapes and Images are 5-fold cross-validated over the set of values {10, 50}, and the powers and bandwidths of the Multiparameter Persistence Images and Kernels are also 5-fold cross-validated with values in {0, 1} (power) and 10{ 2, 1,0,1,2} (bandwidth). |