Differentiating through the Fréchet Mean
Authors: Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, Christopher De Sa
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate this integration, we present two case studies. First, we apply our Fr echet mean to the existing Hyperbolic Graph Convolutional Network, replacing its projected aggregation to obtain state-of-the-art results on datasets with high hyperbolicity. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Cornell University, NY, Ithaca, USA 2Facebook AI, NY, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Poincar e model Fr echet mean algorithm |
| Open Source Code | Yes | Our Py Torch implementation of the differentiable Fr echet mean can be found at https://github.com/CUVL/Differentiable Frechet-Mean. |
| Open Datasets | Yes | Our new aggregation allows us to achieve new state-of-the-art results on the Disease and Disease-M graph datasets (Chami et al., 2019). ... On the rather non-hyperbolic Co RA (Sen et al., 2008) dataset... |
| Dataset Splits | Yes | we test with precisely the same hyperparameters (learning rate, test/val split, and the like) as Chami et al. (2019) for a fair comparison. |
| Hardware Specification | Yes | Experiments were run with an Intel Skylake Core i7-6700HQ 2.6 GHz Quad core CPU. |
| Software Dependencies | No | The paper mentions a 'Py Torch implementation' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use precisely the same architecture as in Chami et al. (2019), except we substitute all hyperbolic aggregation layers with our differentiable Fr echet mean layer. Furthermore, we test with precisely the same hyperparameters (learning rate, test/val split, and the like) as Chami et al. (2019) for a fair comparison. |