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..
Techniques for Symbol Grounding with SATNet
Authors: Sever Topan, David Rolnick, Xujie Si
NeurIPS 2021 | Venue PDF | 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 EMAIL |
| 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]. |