Library for augmenting NN with UQ
Project description
Quantification of Uncertainties in Neural Networks (QUiNN) is a python library centered around various probabilistic wrappers over PyTorch modules in order to provide uncertainty estimation in Neural Network (NN) predictions.
Build the library
./build.sh
or
./setup.py build; setup.py install
Requirements
numpy, scipy, matplotlib, pytorch
Examples
examples/ex_fit.py
examples/ex_fit_2d.py
examples/ex_ufit.py <method> # where method=mcmc, ens or vi.
Authors
Khachik Sargsyan
Javier Murgoitio-Esandi
Oscar Diaz-Ibarra
Acknowledgements
This work is supported by
- U.S. Department of Energy, Office of Fusion Energy Sciences (OFES) under Field Work Proposal Number 20-023149.
- Laboratory Directed Research and Development (LDRD) program of Sandia National Laboratories.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
uqinn-1.0.0.tar.gz
(55.6 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
uqinn-1.0.0-py3-none-any.whl
(69.2 kB
view details)
File details
Details for the file uqinn-1.0.0.tar.gz.
File metadata
- Download URL: uqinn-1.0.0.tar.gz
- Upload date:
- Size: 55.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
07c6a5a81bc14af2679e9510f438ef1bbb85eba00c4fdd993fcb1f018f511cac
|
|
| MD5 |
15b7c184defe9283b053dbb02e52e047
|
|
| BLAKE2b-256 |
0a66f6f43b1584b4016888e9529b8985f47d230f25a9ad59155d24596596a3a9
|
File details
Details for the file uqinn-1.0.0-py3-none-any.whl.
File metadata
- Download URL: uqinn-1.0.0-py3-none-any.whl
- Upload date:
- Size: 69.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ad9b64d5aea1143d802273f338791a93e595d9793cae8a0af88e82c7e4087257
|
|
| MD5 |
3f02d66ad77512e3d8dd1d16522318ae
|
|
| BLAKE2b-256 |
91d43322de2de73299d8a86574069c6e26df031bfcd395a158e161273630ae96
|
