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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Optimizing persistent homology based functions
Authors: Mathieu Carriere, Frederic Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | as well as some experiments showcasing the versatility of our approach. |
| Researcher Affiliation | Collaboration | 1Universit e Cˆote d Azur, Inria, France 2Universit e Paris-Saclay, CNRS, Inria, Laboratoire de Math ematiques d Orsay, France 3Fujitsu Ltd., Kanagawa, Japan. |
| Pseudocode | Yes | Algorithm 1 Persistence pairs computation (sketch) |
| Open Source Code | Yes | It is publicly available at https://github.com/ Mathieu Carriere/difftda |
| Open Datasets | Yes | We classify images from the MNIST data set. Scores do not have standard deviations since we use the train/test splits of the mnist.load data function in Tensor Flow 2. |
| Dataset Splits | Yes | Scores do not have standard deviations since we use the train/test splits of the mnist.load data function in Tensor Flow 2. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Gudhi' and 'Tensor Flow 2' but does not provide specific version numbers for these software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | In this experiment, we start with a point cloud X sampled uniformly from the unit square S = [0, 1]2, and then optimize the point coordinates so that the loss L(X) = P(X) + T(X) is minimized. Then train an autoencoder made of four fully-connected layers with 32 neurons and Re LU activations and using the first five persistence landscapes with resolution 100. |