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Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis · Tom Nicholson · Marc Deisenroth · James Hensman

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #190 #None

The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs has focussed on the state estimation problem, i.e., computing a posterior of the latent state given the model. However, the key challenge in GPSSMs has not been satisfactorily addressed yet: system identification, i.e., learning the model. To address this challenge, we impose a structured Gaussian variational posterior distribution over the latent states, which is parameterised by a recognition model in the form of a bi-directional recurrent neural network. Inference with this structure allows us to recover a posterior smoothed over sequences of data. We provide a practical algorithm for efficiently computing a lower bound on the marginal likelihood using the reparameterisation trick. This further allows for the use of arbitrary kernels within the GPSSM. We demonstrate that the learnt GPSSM can efficiently generate plausible future trajectories of the identified system after only observing a small number of episodes from the true system.

Author Information

Stefanos Eleftheriadis (PROWLER.io)
Tom Nicholson (PROWLER.IO)
Marc Deisenroth (Imperial College London)
James Hensman (PROWLER.io)

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