"Negative initial weights improve learning in recurrent neural networks."

Davor Pavisic et al. (1996)

> Home

Details and statistics

DOI: —

access: open

type: Conference or Workshop Paper

metadata version: 2022-08-02

- view
  - electronic edition @ esann.org (open access)
- export record
  dblp key:
  - conf/esann/PavisicDTCCL96
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/esann/PavisicDTCCL96
Davor Pavisic, Jean-Philippe Draye, Roberto Teran, Gustavo Calderon, Guy Cheron, Gaetan Libert:
Negative initial weights improve learning in recurrent neural networks. ESANN 1996

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.