NeuPSL: Neural Probabilistic Soft Logic
Authors: Connor Pryor, Charles Dickens, Eriq Augustine, Alon Albalak, William Yang Wang, Lise Getoor
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through an extensive empirical evaluation, we demonstrate the benefits of using Ne Sy methods, achieving upwards of 30% improvement over independent neural network models. We perform extensive evaluations over two image classification tasks and two citation network datasets. |
| Researcher Affiliation | Academia | Connor Pryor1 , Charles Dickens1 , Eriq Augustine 1 , Alon Albalak 2 , William Yang Wang 2 and Lise Getoor 1 1 UC Santa Cruz 2 UC Santa Barbara |
| Pseudocode | No | The paper does not contain any structured pseudocode blocks or algorithms explicitly labeled as such. |
| Open Source Code | Yes | Code and Data: https://github.com/linqs/neupsl-ijcai23 |
| Open Datasets | Yes | The first set of experiments are conducted on a variation of MNIST Addition, a widely used Ne Sy evaluation task [Manhaeve et al., 2018]. Inspired by the Visual Sudoku problem proposed by Wang et al. (2019), Augustine et al. (2022) introduced a novel Ne Sy task, Visual-Sudoku-Classification. In our final experiment, we evaluate the performance of Neu PSL on two widely studied citation network node classification datasets: Citeseer and Cora [Sen et al., 2008]. |
| Dataset Splits | Yes | We averaged the results over ten randomly sampled splits using 5% of the nodes for training, 5% of the nodes for validation, and 1000 nodes for testing. |
| Hardware Specification | No | The paper states that "Implementation details, hyperparameters, network architectures, hardware, and Neu PSL models, are described in the Appendix." and provides a link to the appendix, but the specific hardware details are not present within the provided text. |
| Software Dependencies | No | The paper mentions using "the open-source PSL software package" and "TensorFlow" but does not provide specific version numbers for these software dependencies, which are necessary for reproducibility. |
| Experiment Setup | No | The paper states that "Implementation details, hyperparameters, network architectures, hardware, and Neu PSL models, are described in the Appendix." and provides a link to the appendix, but the specific experimental setup details (e.g., hyperparameters) are not present within the provided text. |