跳到主要导航 跳到搜索 跳到主要内容

Reluplex made more practical: Leaky ReLU

  • Jin Xu
  • , Zishan Li
  • , Bowen Du
  • , Miaomiao Zhang*
  • , Jing Liu*
  • *此作品的通讯作者
  • Tongji University
  • University of Warwick

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

In recent years, Deep Neural Networks (DNNs) have been experiencing rapid development and have been widely used in various fields. However, while DNNs have shown strong capabilities, their security problems have gradually been exposed. Therefore, the formal guarantee of neural network output is needed. Prior to the appearance of the Reluplex algorithm, the verification of DNNs was always a difficult problem. Reluplex algorithm is specially used to verify DNNs with ReLU activation function. This is an excellent and effective algorithm, but it cannot verify more activation functions. ReLU activation function will bring about "Dead Neuron"problem, and Leaky ReLU activation function can solve this problem, so it is necessary to verify DNNs based on Leaky ReLU activation function. Therefore, we propose the Leaky-Reluplex algorithm, which is based on the Reluplex algorithm. Leaky-Reluplex algorithm can verify DNNs based on Leaky ReLU activation function.

源语言英语
主期刊名2020 IEEE Symposium on Computers and Communications, ISCC 2020
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9781728180861
DOI
出版状态已出版 - 7月 2020
活动2020 IEEE Symposium on Computers and Communications, ISCC 2020 - Rennes, 法国
期限: 7 7月 202010 7月 2020

丛书

姓名Proceedings - IEEE Symposium on Computers and Communications
2020-July
ISSN(印刷版)1530-1346

会议

会议2020 IEEE Symposium on Computers and Communications, ISCC 2020
国家/地区法国
Rennes
时期7/07/2010/07/20

学术指纹

探究 'Reluplex made more practical: Leaky ReLU' 的科研主题。它们共同构成独一无二的学术指纹。

引用此