Daphne: Multi-Pass Compilation of Probabilistic Programs into Graphical Models and Neural Networks
Abstract: Daphne is a probabilistic programming system that provides an expressive syntax to denote a large, but restricted, class of probabilistic models. Programs written in the Daphne language can be compiled into a general graph data structure of a corresponding probabilistic graphical model with simple link functions that can easily be implemented in a wide range of programming environments. Alternatively Daphne can also further compile such a graphical model into understandable and vectorized PyTorch code that can be used to train neural networks for inference. The Daphne compiler is structured in a layered multi-pass compiler framework that allows independent and easy extension of the syntax by adding additional passes, while leveraging extensive partial evaluation to reduce all syntax extensions to the graphical model at compile time.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Andriy_Mnih1
Submission Number: 3262
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