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Computational Movement Analysis
Uncertainty quantification and out-of-distribution detection using surjective normalizing flows
Dr. Yanan Xin, who leads the Mobility Information Engineering (MIE) Lab, presented a talk at DINAcon 2023 titled Computational Movement Analysis for Sustainable and Intelligent Mobility.
The transport sector is currently experiencing a rapid transformation fueled by disruptive mobility technologies aimed at providing sustainable and intelligent mobility services. To effectively harness the potential of these technologies, it is crucial to gain a deep understanding of human mobility patterns and develop innovative computational methods to analyze vast amounts of mobility data. In this presentation, Dr. Xin will showcase the research conducted at the Mobility Information Engineering Lab on facilitating the adoption of shared electric vehicles and developing casually-enabled interpretable and robust machine learning methods for mobility data analysis.
{ hacknight challenges }
(( a quick challenge for someone to take their first steps as a user... ))
(( something that one could do once familiar with the project... ))
(( a challenge for the coders and pro users in the room... ))
Uncertainty quantification for mobility analysis
About
This repository contains library code for training a surjective normalizing flow for out-of-distribution detection using epistemic uncertainty estimates.
Installation
To install the latest GitHub
pip install git+https://github.com/irmlma/uncertainty-quantification-for-mobility-analysis@<TAG>
This installs the library as well as executables in your current (virtual) environment.
Usage
Having installed as described above you can train and make predictions using the provided exectuables.
Train the model using the provided config file via:
uqma-train
--config=configs/config.py \
--infile=<FILE> \
--outfile=<PARAMS_FILE>
where
To make predictions for epistemic uncertainty estimates, call:
uqma-predict
--params=<PARAMS_FILE> \
--infile=<FILE> \
--outfile=<PARAMS_FILE>
where
Citation
If you find our work relevant to your research, please consider citing
@article{dirmeier2023uncertain,
title={Uncertainty quantification and out-of-distribution detection using surjective normalizing flows},
author={Simon Dirmeier and Ye Hong and Yanan Xin and Fernando Perez-Cruz},
year={2023},
journal={arXiv preprint arXiv:2311.00377}
}
Author
Simon Dirmeier sfyrbnd @ pm me
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