"A Survey of Interpretability of Machine Learning in Accelerator-based High ..."

Danielle Turvill et al. (2020)

> Home

Details and statistics

DOI: 10.1109/BDCAT50828.2020.00025

access: closed

type: Conference or Workshop Paper

metadata version: 2022-10-02

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/bdc/TurvillBYZ20
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/bdc/TurvillBYZ20
Danielle Turvill, Lee Barnby, Bo Yuan, Ali Zahir:
A Survey of Interpretability of Machine Learning in Accelerator-based High Energy Physics. BDCAT 2020: 77-86

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.