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..
Learning Infinitesimal Generators of Continuous Symmetries from Data
Authors: Gyeonghoon Ko, Hyunsu Kim, Juho Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our method mainly in two domains: image data and partial differential equations, and demonstrate its advantages. Our codes are available at https: //github.com/kogyeonghoon/learning-symmetry-from-scratch.git. |
| Researcher Affiliation | Academia | Gyeonghoon Ko, Hyunsu Kim, Juho Lee Kim Jaechul Graduate School of AI KAIST Seoul, South Korea EMAIL |
| Pseudocode | No | The paper describes its methods textually and mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our codes are available at https: //github.com/kogyeonghoon/learning-symmetry-from-scratch.git. |
| Open Datasets | Yes | We use images of size 32 32 from the CIFAR-10 classification task. |
| Dataset Splits | No | The paper details training and test procedures but does not explicitly mention a dedicated validation dataset split or its specific use. |
| Hardware Specification | Yes | The learning process takes less than 10 hours on a Ge Force RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions various software components and methods (e.g., Neural ODE, MLP, ResNet-18, SGD, Adam, WENO scheme) but does not provide specific version numbers for these, which is necessary for reproducible software dependencies. |
| Experiment Setup | Yes | We learn the Equation 14 using stochastic gradient descent with wsym = 1 and wortho, w Lips = 10. The parameter σ, which controls the scale of transformation, is set to σ = 0.4, and the Lipschitz threshold τ is set to τ = 0.5. ... When training the Res Net-18 with CIFAR-10, both the feature extractor Hfext and models after augmentation, we train the model in 200 epochs with a batch size 128. The learning rate is set to 10 1 and decreases by a factor of 0.2 at the 60th, 120th, and 160th epoch. The model is trained by SGD optimizer with Nesterov momentum 0.9 and weight decay 0.4. |