Information Sciences Seminar——Extracting automata from neural networks using active learning
报告人：Zhiwu Xu（Shenzhen University）
时间：2021-11-05 13:00-14:00
地点：腾讯会议 ID：461 307 024
Abstract: Deep learning is one of the most advanced forms of machine learning. Most modern deep learning models are based on an artificial neural network, and benchmarking studies reveal that neural networks have produced results comparable to and in some cases superior to human experts. However, the generated neural networks are typically regarded as incomprehensible black-box models, which not only limits their applications, but also hinders testing and verifying. In this paper, we present an active learning framework to extract automata from neural network classifiers, which can help users to understand the classifiers. In more detail, we use Angluin’s L* algorithm as a learner and the neural network under learning as an oracle, employing abstraction interpretation of the neural network for answering membership and equivalence queries. Our abstraction consists of value, symbol and word abstractions. The factors that may affect the abstraction are also discussed in the paper. We have implemented our approach in a prototype. To evaluate it, we have performed the prototype on a MNIST classifier and have identified that the abstraction with interval number 2 and block size 1 × 28 offers the best performance in terms of F1 score. We also have compared our extracted DFA against the DFAs learned via the passive learning algorithms provided in LearnLib and the experimental results show that our DFA gives a better performance on the MNIST dataset.
Bio：许智武，深圳大学计算机与软件学院副教授，2013年毕业于巴黎第七大学和中国科学院大学（中欧联合培养博士），主要从事程序分析与验证、类型系统、程序安全、自动机理论和逻辑、机器学习等方面的研究工作，2014年获得EAPLS（欧洲程序语言与系统协会）最佳博士论文奖，主持国家自然科学基金面上项目1项、青年基金1项，广东省自然科学基金面上项目1项，已在国际顶级会议POPL、ICSE、ASE、FSE、ICDE、ICFP、FM、AAAI、USENIX等发表多篇论文。
腾讯会议 ID：461 307 024
