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社区首页 >专栏 >DEEP PROBABILISTIC PROGRAMMING 深度概率编程

DEEP PROBABILISTIC PROGRAMMING 深度概率编程

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CreateAMind
发布2018-07-25 10:11:02
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发布2018-07-25 10:11:02
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文章被收录于专栏:CreateAMind

We propose Edward, a Turing-complete probabilistic programming language. Edward builds on two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation, to variational inference, to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. For efficiency, Edward is integrated into TensorFlow, providing significant speedups over existing probabilistic systems. For example, on a benchmark logistic regression task, Edward is at least 35x faster than Stan and PyMC3.


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原始发表:2017-01-18,如有侵权请联系 cloudcommunity@tencent.com 删除

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