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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
Authors: Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed Neuro SAT method. |
| Researcher Affiliation | Industry | Saeed Amizadeh, Sergiy Matusevych, Markus Weimer Microsoft Redmond, WA 98052 EMAIL |
| Pseudocode | No | The paper describes algorithms and models using mathematical formulations and descriptive text, but does not include any explicit pseudocode blocks or labeled algorithm listings. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | We have used the generation process proposed in the Neuro SAT paper Selsam et al. (2018) to generate random k-SAT CNF pairs (with k stochastically set according to the default settings in Selsam et al. (2018)). These pairs are then directly fed to Neuro SAT for training. For our method, on the other hand, we ο¬rst need to convert these CNFs into circuits. |
| Dataset Splits | No | We have trained a DG-DAGRNN model (i.e. our framework) and a Neuro SAT model on a dataset of 300K SAT and UNSAT pairs generated according to the scheme proposed in Selsam et al. (2018). |
| Hardware Specification | No | However, since we ran our experiments only on one GPU with limited memory, we had to limit the training sample size for the purpose of experimentation. |
| Software Dependencies | No | For training, we have used the Adam optimization algorithm with learning rate of 10-5, weight decay of 10-10 and gradient clipping norm of 0.65. |
| Experiment Setup | Yes | For training, we have used the Adam optimization algorithm with learning rate of 10-5, weight decay of 10-10 and gradient clipping norm of 0.65. We have also applied a dropout mechanism for the aggregator function during training with the rate of 20%. For the Neuro SAT model, we have used the default hyper-parameter settings proposed in Selsam et al. (2018). |