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Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation
Versatile movement representations allow robots to learn new tasks and r...
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Increasing the Generalisaton Capacity of Conditional VAEs
We address the problem of one-to-many mappings in supervised learning, w...
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Learning Hierarchical Priors in VAEs
We propose to learn a hierarchical prior in the context of variational a...
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Efficient Low-Order Approximation of First-Passage Time Distributions
We consider the problem of computing first-passage time distributions fo...
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f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Generative neural samplers are probabilistic models that implement sampl...
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Expectation propagation for continuous time stochastic processes
We consider the inverse problem of reconstructing the posterior measure ...
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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...
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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...
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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...
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