PLLay: Efficient Topological Layer based on Persistent Landscapes
Authors: Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Kim, Frederic Chazal, Larry Wasserman
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach by classification experiments on various datasets. |
| Researcher Affiliation | Collaboration | Kwangho Kim Carnegie Mellon University Pittsburgh, USA EMAIL Jisu Kim Inria Palaiseau, France EMAIL Manzil Zaheer Google Research Mountain View, USA EMAIL Joon Sik Kim Carnegie Mellon University Pittsburgh, USA EMAIL Frederic Chazal Inria Palaiseau, France EMAIL Larry Wasserman Carnegie Mellon University Pittsburgh, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Implementation of single structure element for PLLay |
| Open Source Code | Yes | Reproducibility. The code for PLLay is available at https://github.com/jisuk1/pllay/. |
| Open Datasets | Yes | To demonstrate the effectiveness of the proposed approach, we study classification problems on two different datasets: MNIST handwritten digits and ORBIT5K. . . . ORBIT5K dataset [Adams et al., 2017, Carrière et al., 2020]. |
| Dataset Splits | No | The paper specifies training and test set sizes (e.g., 'standard training set consists of 60,000 examples, and test set of 10,000 examples' for MNIST; 'We used 400 instances for training and 100 for testing' for ORBIT5K) but does not explicitly mention a separate validation split or its size. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, etc.) used for running the experiments. |
| Software Dependencies | Yes | The GUDHI Project. GUDHI User and Reference Manual. GUDHI Editorial Board, 3.3.0 edition, 2020. URL https://gudhi.inria.fr/doc/3.3.0/. |
| Experiment Setup | Yes | We refer to Appendix G for details about each simulation setup and our model architectures. . . . MLP model has 2 hidden layers with 100 neurons each. CNN model has two convolutional layers (32 filters, 5x5 kernel size, 2x2 pooling) followed by two fully connected layers (100 neurons each). . . . Adam optimizer with a batch size of 32 and learning rate of 0.001. |