About
A more dedicated study on the information flow in DNNs will help us understand their behaviour and the deep connection between DNN models and the corresponding tasks. Taking into account our well-established physics analysis framework (observable-based), we present a novel way to interpret DNNs results for HEP, which not only gives a clear physics picture but also inspires interfaces with the theoretical foundation. Information captured by DNNs can thus be used as a fine-tailored general-purpose encoder. As a concrete example, we showcase using encoded information to help with physics searches at the LHC..
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The talk
Credits and licence
- Institution
- CERN
- Department
- LPCC Workshops
- Speaker
- Taoli Cheng
- Year
- 2019
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed