Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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 and potential bias; scores should be interpreted as estimates.
Interpreting Multivariate Shapley Interactions in DNNs
Authors: Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang10877-10886
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,963 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,247 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |