Learning Symmetric Rules with SATNet
Authors: Sangho Lim, Eun-Gyeol Oh, Hongseok Yang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with Sudoku and Rubik s cube show the substantial improvement of Sym SATNet over the baseline SATNet. |
| Researcher Affiliation | Academia | Sangho Lim School of Computing KAIST Daejeon, South Korea lim.sang@kaist.ac.kr Eun-Gyeol Oh Graduate School of Information Security KAIST Daejeon, South Korea eun-gyeol.oh@kaist.ac.kr Hongseok Yang School of Computing and Kim Jaechul Graduate School of AI, KAIST Discrete Mathematics Group, Institute for Basic Science (IBS) Daejeon, South Korea hongseok.yang@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1 SYMFIND with a threshold λ > 0 |
| Open Source Code | No | The paper states 'Sym SATNet is implemented based on the SATNet code [26] available under the MIT License.' This refers to a third-party tool they used, not their own source code for Sym SATNet being made publicly available. |
| Open Datasets | Yes | We used 9K training and 1K test examples generated by the Sudoku generator [21]. [21] is Kyubyong Park. Can convolutional neural networks crack sudoku puzzles? https://github. com/Kyubyong/sudoku, 2018. |
| Dataset Splits | Yes | We used 8K training, 1K validation, and 1K test examples to train Sym SATNet with symmetries found by SYMFIND and the validation step. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not provide specific software dependencies with version numbers (e.g., Python version, PyTorch/TensorFlow versions, specific library versions). |
| Experiment Setup | Yes | We used binary cross entropy loss and Adam optimizer [16], with the learning rate η = 2 10 3 for SATNet-Plain and SATNet-300aux as the original work and η = 4 10 2 for Sym SATNet. We trained Sym SATNet, SATNet-Plain, and SATNet-300aux for 100 epochs, under the same configuration as in the Sudoku case. |