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Learning Flat Latent Manifolds with VAEs
Measuring the similarity between data points often requires domain knowl...
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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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Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along the Riemannian ...
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Active Learning based on Data Uncertainty and Model Sensitivity
Robots can rapidly acquire new skills from demonstrations. However, duri...
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Metrics for Deep Generative Models
Neural samplers such as variational autoencoders (VAEs) or generative ad...
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Alexej Klushyn
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