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Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior

We provide the implementation and experiment results for the paper: Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior

examples_construct_representation.ipynb : examples of random functional representation
examples_task1_dataset.ipynb : examples of tasks in section 4.1
examples_task1_singlechannel_regression.ipynb : experiment results in section 4.1
examples_task2_multichannel_regression.ipynb : experiment results in section 4.2
examples_task3_imagecompletion.ipynb : experiment results in section 4.3
examples_task4_predatorpray.ipynb : experiment results in Appendix E

if necessary, install the required module as follows
pip3 install module-name
ex) pip3 install numpy

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
.ipynb_checkpoints		.ipynb_checkpoints
__pycache__		__pycache__
datasets		datasets
download		download
models		models
params		params
README.md		README.md
examples_construct_representation.ipynb		examples_construct_representation.ipynb
examples_task1_signelchannel_dataset.ipynb		examples_task1_signelchannel_dataset.ipynb
examples_task1_singlechannel_regression.ipynb		examples_task1_singlechannel_regression.ipynb
examples_task2_multichannel_regression.ipynb		examples_task2_multichannel_regression.ipynb
examples_task3_imagcompletion.ipynb		examples_task3_imagcompletion.ipynb
examples_task4_predatorpray.ipynb		examples_task4_predatorpray.ipynb
train_completion_imag_temperature.py		train_completion_imag_temperature.py
train_regression_singlechannel.py		train_regression_singlechannel.py