NeurASP
swMATH ID: | 43631 |
Software Authors: | Yang, Zhun; Ishay, Adam; Lee, Joohyung |
Description: | NeurASP is a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. This repository includes examples to show: how NeurASP can make use of pretrained neural networks in symbolic computation and how it can improve the perception accuracy of a neural network by applying symbolic reasoning in answer set programming; and how NeurASP is used to train a neural network better by training with rules so that a neural network not only learns from implicit correlations from the data but also from the explicit complex semantic constraints expressed by ASP rules. |
Homepage: | https://www.ijcai.org/proceedings/2020/0243.pdf |
Source Code: | https://github.com/azreasoners/NeurASP |
Dependencies: | Python |
Related Software: | DeepProbLog; ProbLog; Adam; CLEVR; CLEVR dataset; SMProbLog; DL2; NeuroSAT; PRISM; Clingo; ASSAT; Metagol; VQA; TensorLog; InfoGAN; AI2; IMLI; MLIC; GamePad; HOList |
Cited in: | 13 Documents |
all
top 5
Cited by 47 Authors
Cited in 5 Serials
4 | International Journal of Approximate Reasoning |
2 | Artificial Intelligence |
2 | Machine Learning |
1 | The Journal of Artificial Intelligence Research (JAIR) |
1 | Theory and Practice of Logic Programming |
Cited in 2 Fields
13 | Computer science (68-XX) |
1 | Statistics (62-XX) |