资源分类
》 资源 》论文》Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

2020-03-11 | |  57 |   39 |   0

Abstract

We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin’s L* algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained RNNs, even when the state vectors are large and require fine differentiation.

上一篇：First Order Generative Adversarial Networks

下一篇：PixelSNAIL: An Improved Autoregressive Generative Model

用户评价
全部评价

热门资源

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Rating-Boosted La...

    The performance of a recommendation system reli...

  • Hierarchical Task...

    We extend hierarchical task network planning wi...