equine-webapp 0.1.dev80

Companion web application for EQUINE^2: Establishing Quantified Uncertainty for Neural Networks

EQUINE Webapp

This is a web application utilizing EQUINE for neural network uncertainty quantification through a visual user interface. The webapp allows you to upload your own model and data, and let the server retrain the model with EQUINE. The visualization dashboard also allows you to analyze your samples and view uncertainty quantification visualizations to explain model uncertainty.

The EQUINE repository is here https://github.com/mit-ll-responsible-ai/equine

ScatterUQ at IEEE VIS 2023

We presented ScatterUQ at IEEE VIS 2023: https://ieeexplore.ieee.org/document/10360884

Our data and analysis script can be found in this release: https://github.com/mit-ll-responsible-ai/equine-webapp/releases/tag/ScatterUQ-VIS-2023-Data

React Frontend

ScatterUQ Static Demo

We deployed the frontend application to GitHub pages. You can view a static demo of ScatterUQ here:

https://mit-ll-responsible-ai.github.io/equine-webapp/demo

Development Frontend Server Setup

1. Install node packages

cd client
npm i

2. Start the development server

npm run dev

Python Flask Server

Development Flask Server Setup

1. Create a new Anaconda environment

conda create --name equine-webapp python=3.10

2. Activate your new environment

conda activate equine-webapp

3. Install the necessary packages from pip

pip install -r requirements.txt

4. Start the Flask server

python start_dev_server.py

Flask Testing

If you'd like to run our flask tests:

1. Install pytest

pip install pytest

2. Run pytest

python -m pytest

Bibliography

@INPROCEEDINGS{10360884,
  author={Li, Harry X. and Jorgensen, Steven and Holodnak, John and Wollaber, Allan B.},
  booktitle={2023 IEEE Visualization and Visual Analytics (VIS)}, 
  title={ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems}, 
  year={2023},
  volume={},
  number={},
  pages={246-250},
  keywords={Deep learning;Dimensionality reduction;Training;Uncertainty;Visual analytics;Soft sensors;Interactive systems;Uncertainty quantification;Machine learning;Dimensionality reduction;Visualization;Explainable AI},
  doi={10.1109/VIS54172.2023.00058}}

Disclaimer

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

© 2023 MASSACHUSETTS INSTITUTE OF TECHNOLOGY

• Subject to FAR 52.227-11 – Patent Rights – Ownership by the Contractor (May 2014)
• SPDX-License-Identifier: MIT

This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering.

The software/firmware is provided to you on an As-Is basis.