DeepAI AI Chat
Log In Sign Up

Active Learning in Gaussian Process State Space Model

by   Hon Sum Alec Yu, et al.

We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we employ mutual information as our active learning criterion. In particular, we present two approaches for the approximation of mutual information for the GPSSM given latent states. The proposed approaches are evaluated in several physical systems where we actively learn the underlying non-linear dynamics represented by the state-space model.


page 1

page 2

page 3

page 4


Localized active learning of Gaussian process state space models

The performance of learning-based control techniques crucially depends o...

Actively Learning Gaussian Process Dynamics

Despite the availability of ever more data enabled through modern sensor...

Neural Physicist: Learning Physical Dynamics from Image Sequences

We present a novel architecture named Neural Physicist (NeurPhy) to lear...

Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields

Many real-world datasets can be represented in the form of a graph whose...

Active-learning-based non-intrusive Model Order Reduction

The Model Order Reduction (MOR) technique can provide compact numerical ...

Exploiting a Zoo of Checkpoints for Unseen Tasks

There are so many models in the literature that it is difficult for prac...

InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics

The goal of system identification is to learn about underlying physics d...