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Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation
Versatile movement representations allow robots to learn new tasks and r...
01/29/2021 ∙ by Felix Frank, et al. ∙
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Increasing the Generalisaton Capacity of Conditional VAEs
We address the problem of one-to-many mappings in supervised learning, w...
08/23/2019 ∙ by Alexej Klushyn, et al. ∙
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Learning Hierarchical Priors in VAEs
We propose to learn a hierarchical prior in the context of variational a...
05/13/2019 ∙ by Alexej Klushyn, et al. ∙
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Efficient Low-Order Approximation of First-Passage Time Distributions
We consider the problem of computing first-passage time distributions fo...
06/01/2017 ∙ by David Schnoerr, et al. ∙
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f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Generative neural samplers are probabilistic models that implement sampl...
06/02/2016 ∙ by Sebastian Nowozin, et al. ∙
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Expectation propagation for continuous time stochastic processes
We consider the inverse problem of reconstructing the posterior measure ...
12/18/2015 ∙ by Botond Cseke, et al. ∙
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Properties of Bethe Free Energies and Message Passing in Gaussian Models
We address the problem of computing approximate marginals in Gaussian pr...
01/16/2014 ∙ by Botond Cseke, et al. ∙
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Factored expectation propagation for input-output FHMM models in systems biology
We consider the problem of joint modelling of metabolic signals and gene...
05/17/2013 ∙ by Botond Cseke, et al. ∙
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Sparse Approximate Inference for Spatio-Temporal Point Process Models
Spatio-temporal point process models play a central role in the analysis...
05/17/2013 ∙ by Botond Cseke, et al. ∙
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