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Embedded-Model Flows for Probabilistic Inference by jacksonns.br is a document available to read on EtoBox.

What is Embedded-Model Flows for Probabilistic Inference about?

This preprint proposes embedded-model flows (EMF), which combine the inductive biases of model-free deep learning and explicit probabilistic modeling. EMFs alternate between general-purpose transformations from normalizing flows and structured layers constructed by converting user-specified probabilistic models into equivalent bijective transformations. This allows domain knowledge to be incorporated while correcting model deficiencies. EMFs enable properties like multimodality and hierarchical coupling. Th

Author

jacksonns.br

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EN