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Klamer Schutte

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2 papers

2 author rows

NeurIPS Conference 2019 Conference Paper

The Functional Neural Process

Christos Louizos
Xiahan Shi
Klamer Schutte
Max Welling

We present a new family of exchangeable stochastic processes, the Functional Neural Processes (FNPs). FNPs model distributions over functions by learning a graph of dependencies on top of latent representations of the points in the given dataset. In doing so, they define a Bayesian model without explicitly positing a prior distribution over latent global parameters; they instead adopt priors over the relational structure of the given dataset, a task that is much simpler. We show how we can learn such models from data, demonstrate that they are scalable to large datasets through mini-batch optimization and describe how we can make predictions for new points via their posterior predictive distribution. We experimentally evaluate FNPs on the tasks of toy regression and image classification and show that, when compared to baselines that employ global latent parameters, they offer both competitive predictions as well as more robust uncertainty estimates.

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IROS Conference 2010 Conference Paper

Efficient trajectory bending with applications to loop closure

Gijs Dubbelman
Isaac Esteban
Klamer Schutte

In robotic applications the absolute pose is often obtained as the integral of successive relative rigid-body motions. As each relative rigid-body motion is typically the product of statistical inference, the integrated absolute pose will exhibit error build-up and the estimated trajectory will differ from the true trajectory undertaken by the system. Some application areas allow the system to receive additional information about its current absolute pose, for example from loop detection, which is more accurate than the integral of the relative rigid-body motions. The availability of this absolute information is usually less frequent than the information underlying the relative rigid-body motions. This contribution addresses an efficient closed form algorithm which minimally bends a trajectory such that the integrated pose is exactly equal to any particular desired pose. The manner in which the bending is distributed over the trajectory is controllable using weights. The proposed method will be compared against a maximum likelihood solution on simulated trajectories as well as on trajectories estimated from binocular and monocular data. The results indicate that the performance differences between the closed form approach and the maximum likelihood solution are negligible while the closed form approach is significantly more efficient.

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