Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The Numerical Stability of Hyperbolic Representation Learning
Authors: Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments |
| Researcher Affiliation | Academia | 1Halฤฑcฤฑo 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. |