Learning Infinitesimal Generators of Continuous Symmetries from Data
Authors: Gyeonghoon Ko, Hyunsu Kim, Juho Lee
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 {kog, kim.hyunsu, juholee}@kaist.ac.kr |
| 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. |