Zhu, Zhiyu and Chen, Huaming and Zhang, Jiayu and Wang, Xinyi and Jin, Zhibo and Xue, Minhui and Zhu, Dongxiao and Choo, Kim-Kwang Raymond (2024) MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks. In: 38th Annual AAAI Conference on Artificial Intelligence, Vol 38 No 15, 20-27 February 2024, Vancouver, CANADA.
Full text not available from this repository.Abstract
To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the axioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the existing attribution methods present challenges for effective interpretation and efficient computation. In this work, we introduce MFABA, an attribution algorithm that adheres to axioms, as a novel method for interpreting DNN. Additionally, we provide the theoretical proof and in-depth analysis for MFABA algorithm, and conduct a large scale experiment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art attribution algorithms. The effectiveness of MFABA is thoroughly evaluated through the statistical analysis in comparison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | UNSPECIFIED |
| Additional Information: | 38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence, Vancouver, CANADA, FEB 20-27, 2024 |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Universiti Malaya |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Jan 2025 01:39 |
| Last Modified: | 17 Jan 2025 01:39 |
| URI: | http://eprints.um.edu.my/id/eprint/47665 |
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