Towards Automating Model Explanations with Certified Robustness Guarantees
Authors: Mengdi Huai, Jinduo Liu, Chenglin Miao, Liuyi Yao, Aidong Zhang6935-6943
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct extensive experiments on real-world datasets to verify the desirable properties of the proposed method.Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed interpretation method. |
| Researcher Affiliation | Collaboration | Mengdi Huai1, Jinduo Liu2, Chenglin Miao3, Liuyi Yao4, Aidong Zhang1 1 University of Virginia 2 Beijing University of Technology 3 University of Georgia 4 Alibaba Group |
| Pseudocode | No | No clearly labeled pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to code repositories. |
| Open Datasets | Yes | Here we adopt three image datasets: the MNIST (Le Cun et al. 1998), CIFAR-10 (Recht et al. 2018), and AT&T (Chopra, Hadsell, and Le Cun 2005) datasets. |
| Dataset Splits | Yes | Table 1: The statistic information of the adopted datasets. ... #Training 55,000 #Validation 5,000 #Testing 10,000 |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | Due to space limitations, the parameter settings, the description of the network architectures and more experiment results will be given in the full version of the paper. |