PLLay: Efficient Topological Layer based on Persistent Landscapes
Authors: Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Kim, Frederic Chazal, Larry Wasserman
NeurIPS 2020 | Conference 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 kwanghk@cmu.edu Jisu Kim Inria Palaiseau, France jisu.kim@inria.fr Manzil Zaheer Google Research Mountain View, USA manzilzaheer@google.com Joon Sik Kim Carnegie Mellon University Pittsburgh, USA joonsikk@cs.cmu.edu Frederic Chazal Inria Palaiseau, France frederic.chazal@inria.fr Larry Wasserman Carnegie Mellon University Pittsburgh, USA larry@stat.cmu.edu |
| 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. |