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High-Quality Prediction Intervals for Deep Learning
Code accompanying the ICML 2018 paper High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach https://arxiv.org/abs/1802.07167.
Intro
How can we get uncertainty estimates from deep learning systems?
<sub> Estimating model uncertainty. </sub>
<img width="500" src="images/intro_model_unc.png"><sub> Comparison against MVE.</sub>
<img width="500" src="images/intro_4_grid.png">Jupyter Notebook Keras Demo
A simple fast demo using Keras is included in QD_AsFastAsPoss_notebook.ipynb.
Code Structure
Main paper code in 5 files:
- main.py
- pso.py
- DataGen.py
- DeepNetPI.py
- utils.py
- inputs.txt
Run main.py to reproduce first figure.
We have included hyperparameters used for the boston and concrete datasets in inputs.txt.