pytorch_gum_uncertainty_propagation
v0.17.0
Getting started:
GUM-compliant_neural-network_uncertainty-propagation
Getting started
Documentation
Disclaimer
License
Detailed information:
Installation
Quick setup (
not recommended
)
Updating to the newest version
Proper Python setup with virtual environment (
recommended
)
Set up a virtual environment
Create a
venv
Python environment on Windows
Create a
venv
Python environment on Mac & Linux
Install pytorch_gum_uncertainty_propagation via
pip
Optional Jupyter Notebook dependencies
Install known to work dependencies’ versions
Optional dependencies
Changelog
v0.17.0 (2023-01-20)
Feature
v0.16.0 (2023-01-20)
Feature
Documentation
v0.15.0 (2023-01-17)
Feature
Documentation
v0.14.0 (2022-12-21)
Feature
Documentation
v0.13.0 (2022-12-21)
Feature
Documentation
v0.12.0 (2022-12-21)
Feature
Fix
Documentation
v0.11.0 (2022-12-21)
Feature
Fix
v0.10.0 (2022-12-20)
Feature
Fix
Documentation
Performance
v0.9.0 (2022-12-17)
Feature
v0.8.0 (2022-12-17)
Feature
Documentation
v0.7.0 (2022-12-15)
Feature
Documentation
v0.6.0 (2022-12-14)
Feature
Fix
v0.5.0 (2022-12-11)
Feature
v0.4.0 (2022-12-11)
Feature
Documentation
v0.3.0 (2022-12-10)
Feature
Documentation
v0.2.0 (2022-12-09)
Feature
Documentation
v0.1.0 (2022-12-03)
Feature
Examples:
Examples
Proof-of-Concept examples
Script to propagate inputs for several architectures and activations
Utility module to construct valid covariance matrices from standard uncertainties
Code Reference:
Modules
Functionals
Uncertainties
Propagate through MLPs
ZeMA dataset API
pytorch_gum_uncertainty_propagation
Propagate through MLPs
Edit on GitHub
Propagate through MLPs