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Deep Normalizing Flows for State Estimation

Installation

All the required Python packages are saved in requirements.txt and can be installed via

pip3 install -r requirements.txt

The code is based on PyTorch 1.14 dev. Since the nightly builds of Pytorch can't (at least to my knowledge) easily installed through a requirement file, executing the install_pytorch_nightly.sh file should install the nighlty build of pytorch as well the nflows package. Important: We use our own fork of the nflows package for our custom conditioner functions (i.e., Transformers).

bash install_pytorch_nightly.sh

Running the nflows code

All of the training files have descriptive names. nflows_transformer.py, e.g., is the file for the training of normalizing flows with a Transformer embedding network. Other code follows the same structure.

Results

When running either of the nflows_*.py files, a gif showing the performance of the trained normalzing flow will be saved in ./figs/experiments/*/seq.gif where * is a placeholder for the experiment name. To demonstrate how well normalizing flows work, even for the harder bimodal dataset conditioning on a sequence of noisy observations, we include a gif showing the predictions made using a normalizing flow with a Transformer-embedding.

animated

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CS230 Final Project - UnderCurrent

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