Interpreting Multivariate Shapley Interactions in DNNs
Authors: Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang10877-10886
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted experiments with various DNNs. Experimental results have demonstrated the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang Shanghai Jiao Tong University, China {1603023-zh,xieyichen,bugatti,zizhan52,zqs1022}@sjtu.edu.cn |
| Pseudocode | No | The paper describes the method and uses mathematical equations but does not present structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or direct links to code repositories. |
| Open Datasets | Yes | binary sentiment classification based on the SST-2 dataset (Socher et al. 2013) and prediction of linguistic acceptability based on the Co LA dataset (Warstadt, Singh, and Bowman 2018). |
| Dataset Splits | No | The paper references the SST-2 and CoLA datasets but does not explicitly provide specific training, validation, or test split percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We found that the estimated significance of interactions was accurate enough after the training of 100 epochs. |