This repository hosts code for Variational Auto-Regressive Gaussian Processes for Continual Learning by Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui.
From the root of the directory
conda env create
This creates the environment from environment.yml.
In addition, ensure availability of the this directory in PYTHONPATH
for module imports. As an example in Bash,
export PYTHONPATH="$(pwd):${PYTHONPATH}"
All experiment scripts utilize Fire and the arguments can directly be converted to CLI arguments. Appropriate functions are mentioned to look for CLI arguments to change. The default arguments are enough to reproduce results in the paper.
CLI Arguments: See toy
method.
python experiments/vargp.py toy
CLI Arguments: See split_mnist
method.
python experiments/vargp.py s-mnist
CLI Arguments: See permuted_mnist
method.
python experiments/vargp.py p-mnist
The checkpoints files can be downloaded here. Extract
contents of the zip file into notebooks/results
.
All graphs in the paper can now be generated via code in the notebooks.
SK was supported by the Uber AI Residency program.
Apache 2.0