Techniques for Symbol Grounding with SATNet
Authors: Sever Topan, David Rolnick, Xujie Si
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our method allows SATNet to attain full accuracy even with a harder problem setup that prevents any label leakage. We additionally introduce a proofreading method that further improves the performance of SATNet architectures, beating the state-of-the-art on Visual Sudoku. All experiments were carried out on a Nvidia GTX1070 across 100 epochs, with each epoch taking roughly 2 minutes. Table 1: Performance of our method compared to the original SATNet architecture between grounded and ungrounded versions of the Visual Sudoku problem. |
| Researcher Affiliation | Collaboration | Sever Topan1, 2, David Rolnick1, 3, 4, and Xujie Si1, 3, 4 1Mc Gill University, 2NVIDIA, 3Mila Quebec AI Institute, 4CIFAR AI Research Chair {stopan, drolnick, xsi}@cs.mcgill.ca |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Included as Supplemental Material |
| Open Datasets | Yes | We used the Sudoku Dataset made available under an MIT License from the original SATNet work [7]. |
| Dataset Splits | No | The paper mentions early stopping based on per-cell error, implying a validation step, but does not provide specific details on the dataset split for validation: 'One thing to note is that the self-grounded training step is susceptible to overfitting, and one needs to employ early stopping on the basis of per-cell error in order to learn the permutation matrix ˆP.' |
| Hardware Specification | Yes | All experiments were carried out on a Nvidia GTX1070 across 100 epochs, with each epoch taking roughly 2 minutes. |
| Software Dependencies | No | The paper mentions 'The Adam optimiser was used' but does not specify version numbers for Adam or other key software libraries like PyTorch, TensorFlow, or Python. |
| Experiment Setup | Yes | All experiments were carried out on a Nvidia GTX1070 across 100 epochs, with each epoch taking roughly 2 minutes. The Adam optimiser was used with learning rate of 2 10 3 for the SATNet layer, and 10 5 for the digit classifier [36]. |