Optimizing persistent homology based functions
Authors: Mathieu Carriere, Frederic Chazal, Marc Glisse, Yuichi Ike, Hariprasad Kannan, Yuhei Umeda
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |