Enabling Knowledge Refinement upon New Concepts in Abductive Learning
Authors: Yu-Xuan Huang, Wang-Zhou Dai, Yuan Jiang, Zhi-Hua Zhou
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three neuro-symbolic learning tasks verified the effectiveness of the proposed approach. All experiments are repeated ten times on a server with Intel Xeon Gold 6242R CPU and Nvidia RTX 3090 GPU. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {huangyx, daiwz, jiangy, zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 ABLnc; Algorithm 2 Conflict Resolution; Algorithm 3 Matching Knowledge Graph |
| Open Source Code | Yes | The code is available for download1. 1https://github.com/AbductiveLearning/ABLnc |
| Open Datasets | Yes | The dataset consists of 10k pairs of images, where the digits are randomly generated and their images are randomly sampled from the training set of MNIST. |
| Dataset Splits | Yes | The hyperparameters of ABLnc are determined by cross-validation on training data. |
| Hardware Specification | Yes | All experiments are repeated ten times on a server with Intel Xeon Gold 6242R CPU and Nvidia RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions software components such as CNN, LOF, ASP, ILASP, and Transformer, but does not provide specific version numbers for any of these dependencies. |
| Experiment Setup | No | The paper mentions that 'The hyperparameters of ABLnc are determined by cross-validation on training data' but does not provide specific values for these hyperparameters, training configurations, or other system-level settings. |