Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
| Main Authors: | Michael Weiss, Paolo Tonella |
|---|---|
| Format: | info Proceeding Journal |
| Bahasa: | eng |
| Terbitan: |
, 2021
|
| Online Access: |
https://zenodo.org/record/4651517 |
Daftar Isi:
- Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervision. We present uncertainty-wizard, a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and easy-to-understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.
- Backup Recording for the Talk @ ICST 2021