"Benchmarking and feasibility aspects of machine learning in space systems."

Amir Raoofy et al. (2022)

> Home

Details and statistics

DOI: 10.1145/3528416.3530986

access: closed

type: Conference or Workshop Paper

metadata version: 2022-05-05

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/cf/RaoofyDSG0T22
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/cf/RaoofyDSG0T22
Amir Raoofy, Gabriel Dax, Vittorio Serra, Max Ghiglione, Martin Werner, Carsten Trinitis:
Benchmarking and feasibility aspects of machine learning in space systems. CF 2022: 225-226

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.