The Numerical Stability of Hyperbolic Representation Learning
Authors: Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments |
| Researcher Affiliation | Academia | 1Halıcıo glu Data Science Institute, University of California San Diego, La Jolla, California, USA 2Harvard John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, Massachusetts, USA. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It provides mathematical derivations and descriptions of methods in text and equations. |
| Open Source Code | Yes | Code for reproducing our experiments is available at https://github.com/yangshengaa/stable-hyperbolic. |
| Open Datasets | Yes | We tested the performances on three datasets: CIFAR-10 (Krizhevsky et al., 2009), fashion-MNIST (Xiao et al., 2017), Paul Myeloid Progenitors developmental dataset (Paul et al., 2015), Olsson Single-Cell RNA sequencing dataset (Olsson et al., 2016), Krumsiek Simulated Myeloid Progenitors (Krumsiek et al., 2011), and Moignard blood cell developmental trace from single-cell gene expression (Moignard et al., 2015). |
| Dataset Splits | No | For each dataset, we fix a train-test split and run 5 times. ... For all other datasets, we utilize a 75%/25% train-test split stratified based on the class assignments. The paper does not explicitly mention a separate validation dataset split or provide details for how such a split would be performed. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using 'scikit-learn (Pedregosa et al., 2011)' and 'Py Torch (Paszke et al., 2017)' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use Riemannian SGD (Becigneul & Ganea, 2018) for hyperbolic models and SGD for the Euclidean model, fixing a learning rate of 1 and train for 30000 epochs. ... The best performances of the Euclidean and Poincar e SVM are both using C = 5, with a learning rate of 0.001 and 3000 epochs. ... the best performance of LSVM and LSVMPP are in general brought by C = 0.5, with a learning rate around 10^-10 (depending on the initial scale of the dataset) with 500 epochs. |