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Neural ODE Processes

Official code for the paper Neural ODE Processes (ICLR 2021).

Abstract

Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data-points, a fundamental requirement for real-time applications imposed by the natural direction of time. Second, time-series are often composed of a sparse set of measurements that could be explained by many possible underlying dynamics. NODEs do not capture this uncertainty. In contrast, Neural Processes (NPs) are a new class of stochastic processes providing uncertainty estimation and fast data-adaptation, but lack an explicit treatment of the flow of time. To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs. By maintaining an adaptive data-dependent distribution over the underlying ODE, we show that our model can successfully capture the dynamics of low-dimensional systems from just a few data-points. At the same time, we demonstrate that NDPs scale up to challenging high-dimensional time-series with unknown latent dynamics such as rotating MNIST digits.

@inproceedings{
    norcliffe2021neural,
    title={Neural {\{}ODE{\}} Processes},
    author={Alexander Norcliffe and Cristian Bodnar and Ben Day and Jacob Moss and Pietro Li{\`o}},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=27acGyyI1BY}
}

Getting started

For development, we used Python 3.8.5 and PyTorch 1.8. First, install PyTorch and torchvision using the official page and then run the following command to install the requited packages:

pip install -r requirements.txt

Running the experiments

To run the 1D regression experiments, run one of the following commands:

python -m main.1d_regression --model ndp --exp_name ndp_sine --data sine --epochs 30
python -m main.1d_regression --model ndp --exp_name ndp_exp --data exp --epochs 30
python -m main.1d_regression --model ndp --exp_name ndp_linear --data linear --epochs 30
python -m main.1d_regression --model ndp --exp_name ndp_oscil --data oscil --epochs 30

To run the 2D regression experiments use one of the following

python -m main.2d_regression --model ndp --exp_name ndp_lv --data deterministic_lv --epochs 100
python -m main.2d_regression --model ndp --exp_name ndp_hw --data handwriting --epochs 100

To run the high-dimensional regression experiments use:

python -m main.img_regression --model ndp --exp_name ndp_vrm --data VaryRotMNIST --use_y0 --epochs 50
python -m main.img_regression --model ndp --exp_name ndp_rr --data RotMNIST --use_y0 --epochs 50

Datasets

To use the rotating MNIST datasets, run the script below in order to download the required data:

bash data/download_datasets.sh

Credits

Our code relies to a great extent on the Neural Process implementation by Emilien Dupont. The RotMNIST dataset code adapts the ODE2VAE code.