to be updated...
Run this Google Colab.
or
notebook in density_regression.ipynyb
or
python file (full comparision, install prerequisite packages first to import library):
python demo/run_cubic.py
python demo/density_regression.py
Install prerequisite packages:
pip install -r requirements.txt
Download dataset:
bash depth_estimation/download_data.sh
python <method_file> --exp_idx=<idx>
where the parameters are the following:
<method_file>
: file stored the code of method. E.g.,<method_file> = time_series/density_regression.py
<idx>
: index of experiment. E.g.,<idx> = 1
python uci/main.py --datasets=<dataset_name>
where the parameters are the following:
<dataset_name>
: name of the sub-dataset in UCI. E.g.,<dataset_name> = "wine"
python depth_estimation/main.py --model=<method_name>
where the parameters are the following:
<method_name>
: name of method. E.g.,<method_name> = "densityregressor"
Based on code of:
Time series forecasting
TensorFlow.
Evidential Deep Learning
Amini, Alexander and Schwarting, Wilko and Soleimany, Ava and Rus, Daniela.
arXiv:1910.02600.
Methods for comparing uncertainty quantifications for material property predictions
Kevin Tran, Willie Neiswanger, Junwoong Yoon, Eric Xing, Zachary W. Ulissi.
arXiv:1912.10066.
This source code is released under the Apache-2.0 license, included here.