Factor Graph-based Interpretable Neural Networks
Authors: Yicong Li, Kuanjiu Zhou, Shuo Yu, Qiang Zhang, Renqiang Luo, Xiaodong Li, Feng Xia
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on three datasets and experimental results illustrate the superior performance of AGAIN compared to state-of-the-art baselines. |
| Researcher Affiliation | Academia | 1Dalian University of Technology, 2Jilin University, 3RMIT University |
| Pseudocode | Yes | The overall algorithm for the interactive intervention switch is summarized in Algorithm 1. |
| Open Source Code | Yes | 1Source codes are available at https://github.com/yushuowiki/AGAIN. |
| Open Datasets | Yes | We evaluated AGAIN on two real-world datasets, CUB, MIMIC-III EWS, and one synthetic dataset, Synthetic-MNIST. CUB (Caltech-UCSD Birds-200-2011) dataset ... Footnote 3: http://www.vision.caltech.edu/visipedia/CUB-200.html MIMIC-III EWS dataset ... Footnote 4: https://physionet.org/content/mimiciii/1.4/ Synthetic-MNIST dataset is a composite dataset derived from the original MNIST dataset. Footnote 2: http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | No | The paper mentions using a "test set" in Section 5.2 and a "training set" in Appendix C.5, but does not provide specific percentages or counts for training/test/validation splits. |
| Hardware Specification | Yes | All data processing and experiments are executed on a server with two Xeon-E5 processors, two RTX4000 GPUs and 64G memory. |
| Software Dependencies | Yes | AGAIN is implemented in Py Torch 1.1.0 based on Python 3.7.13. We construct G by instantiating the G as a Markov logic network in Pracmln 1.2.4. |
| Experiment Setup | Yes | We train the concept predictor (real-world datasets) for 500 epochs, the concept predictor (Synthetic-MNIST) for 30 epochs, and the category predictor for 15 epochs. We leverage the sgd optimizer with a learning rate of 0.01 to optimize the model. We to mitigate the overfitting, weight decay of 0.00004 was configured. In the experiment, is set to 0.9. |