Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction
Neural networks are becoming a popular tool for solving many real-world problems such as object recognition and machine translation, thanks to its exceptional performance as an end-to-end solution. However, neural networks are complex black-box models, which hinders humans from interpreting and consequently trusting them in making critical decisions. Towards interpreting neural networks, several approaches have been proposed to extract simple deterministic models from neural networks. The results are not encouraging (e.g., low accuracy and limited scalability), fundamentally due to the limited expressiveness of such simple models.
In this work, we propose an approach to extract probabilistic automata for interpreting an important class of neural networks, i.e., recurrent neural networks. Our work distinguishes itself from existing approaches in two important ways. One is that probability is used to compensate for the loss of expressiveness. This is inspired by the observation that human reasoning is often `probabilistic’. The other is that we adaptively identify the right level of abstraction so that a simple model is extracted in a request-specific way. We conduct experiments on several real-world datasets using state-of-the-art architectures including GRU and LSTM. The result shows that our approach significantly improves existing approaches in terms of accuracy or scalability. Lastly, we demonstrate the usefulness of the extracted models through detecting adversarial texts.
Wed 23 SepDisplayed time zone: (UTC) Coordinated Universal Time change
01:10 - 02:10
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Qianyu Guo College of Intelligence and Computing, Tianjin University, Xiaofei Xie Nanyang Technological University, Yi Li Nanyang Technological University, Singapore, Xiaoyu Zhang Xi'an Jiaotong University, Yang Liu Nanyang Technological University, Singapore, Li Xiaohong TianJin University, Chao Shen Xi'an Jiaotong University
|Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction|
Guoliang Dong Computer College of Zhejiang University, Jingyi Wang Zhejiang University, Jun Sun Singapore Management University, Yang Zhang Zhejiang University, Xinyu Wang Zhejiang University, Dai Ting Huawei International Pte Ltd, Jin Song Dong National University of Singapore, Xingen Wang Zhejiang University
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