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Simple, Distributed, and Accelerated Probabilistic Programming
[article]
2018
arXiv
pre-print
We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single abstraction---the random variable. Our lightweight implementation in TensorFlow enables numerous applications: a model-parallel variational auto-encoder (VAE) with 2nd-generation tensor processing units (TPUv2s); a data-parallel autoregressive model (Image Transformer) with TPUv2s; and multi-GPU No-U-Turn Sampler
arXiv:1811.02091v2
fatcat:gzfjqqs4ujfzllyskht4v3al64