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  • title: Functional tensors for probabilistic programming
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            Functional tensors for probabilistic programming
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            Functional tensors for probabilistic programming

            Dec 14, 2019

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            Fritz Obermeyer

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            Machine learning researchers often express complex models as a program, relying on program transformations to add functionality. New languages and transformations (e.g., TorchScript and TensorFlow AutoGraph) are becoming core capabilities of ML libraries. However, existing transformations, such as automatic differentiation (AD or autodiff), inference in probabilistic programming languages (PPLs), and optimizing compilers are often built in isolation, and limited in scope. This workshop aims at v…

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            Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

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