• Corpus ID: 238634570

Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling

@article{Silvestri2021EmbeddedmodelFC,
  title={Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling},
  author={Gianluigi Silvestri and Emily Fertig and David A. Moore and Luca Ambrogioni},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.06021}
}
Normalizing flows have shown great success as general-purpose density estimators. However, many real world applications require the use of domain-specific knowledge, which normalizing flows cannot readily incorporate. We propose embedded-model flows (EMF), which alternate general-purpose transformations with structured layers that embed domain-specific inductive biases. These layers are automatically constructed by converting user-specified differentiable probabilistic models into equivalent… 
1 Citations
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