Uber Open Sources Pyro, a Deep Probabilistic Programming Language
Achieving Uber’s goal of bringing reliable transportation to everyone requires effortless prediction and optimization at every turn. Opportunities range from matching riders to drivers, to suggesting optimal routes, finding sensible pool combinations, and even creating the next generation of intelligent vehicles. To solve these challenges, we are combining state-of-the-art artificial intelligence (AI) techniques with the rich expertise of data scientists, engineers, and other users. We are exploring a tool-first approach that will enable us and others to make the next generation of AI solutions.
As part of this initiative, Uber AI Labs is excited to announce the open source release of our Pyro probabilistic programming language! Pyro is a tool for deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. The goal of Pyro is to accelerate research and applications of these techniques, and to make them more accessible to the broader AI community.
Pyro builds on the excellent PyTorch library, which includes automatic differentiation using very fast, GPU-accelerated tensor math. PyTorch constructs gradients dynamically, which enables Pyro programs to include stochastic control structure, that is, random choices in a Pyro program can control the presence of other random choices in the program. Stochastic control structure is crucial to make a PPL universal. Hence, Pyro can represent any probabilistic model, while providing automatic optimization-based inference that is flexible and scalable to large data sets.
In Pyro, both the generative models and the inference guides can include deep neural networks as components. The resulting deep probabilistic models have shown great promise in recent work, especially for unsupervised and semi-supervised machine learning problems.
Uber AI Labs déploie son premier projet open source : le langage de programmation probabiliste Pyro
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