PROBABILISTIC CONNECTION IMPORTANCE INFERENCE AND LOSSLESS COMPRESSION OF DEEP NEURAL NETWORKS

Xin Xing, Long Sha, Pengyu Hong, Zuofeng Shang, Jun S. Liu

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations

Abstract

Deep neural networks (DNNs) can be huge in size, requiring a considerable amount of energy and computational resources to operate, which limits their applications in numerous scenarios. It is thus of interest to compress DNNs while maintaining their performance levels. We here propose a probabilistic importance inference approach for pruning DNNs. Specifically, we test the significance of the relevance of a connection in a DNN to the DNN's outputs using a nonparemetric scoring test and keep only those significant ones. Experimental results show that the proposed approach achieves better lossless compression rates than existing techniques.

Original languageEnglish (US)
StatePublished - 2020
Event8th International Conference on Learning Representations, ICLR 2020 - Addis Ababa, Ethiopia
Duration: Apr 30 2020 → …

Conference

Conference8th International Conference on Learning Representations, ICLR 2020
Country/TerritoryEthiopia
CityAddis Ababa
Period4/30/20 → …

All Science Journal Classification (ASJC) codes

  • Education
  • Linguistics and Language
  • Language and Linguistics
  • Computer Science Applications

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