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Neural ODE Processes

23 Mar 2021ICLR 2021 1arXiv:2103.12413archive 2025-07-28

Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò

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 family of models 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.

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count_parameters crisbodnar/ndp/main/1d_regression.py official repository ran · honoured contract MIT (permissive) · f6b944f50d3f15ae · report
MLP theophilec/Reproducing-Neural-ODE-Processes/src/models.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted no licence file found · pointer only · 9b02c90f5ea3f9b2 · report
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get_mask_dim csjtx1021/neural_ode_processes_for_network_dynamics-master/NDP4ND.py community ran · honoured contract MIT (permissive) · 0e802a743acd9f7d · report
t2v csjtx1021/neural_ode_processes_for_network_dynamics-master/NDP4ND.py community ran · fixture could not drive it MIT (permissive) · 4827bf624c11e4fc · report

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