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University of Cambridge > Talks.cam > Machine Learning @ CUED > Probabilistic computing: computation as universal stochastic inference, not deterministic calculation

## Probabilistic computing: computation as universal stochastic inference, not deterministic calculationAdd to your list(s) Download to your calendar using vCal - Vikash K. Mansinghka (MIT)
- Thursday 23 February 2012, 12:30-13:30
- Engineering Department, CBL Room BE-438.
If you have a question about this talk, please contact Zoubin Ghahramani. Latent variable modeling and Bayesian inference are appealing in theory—- they provide a unified mathematical framework for solving a wide range of machine learning problems—- but are often difficult to apply effectively in practice. Accurate inference in even simple models can seem computationally intractable, while more realistic models are difficult to even write down precisely. In this talk, I will introduce new probabilistic programming
technology that aims to alleviave these difficulties. Unlike
graphical models, which marries statistics with graph theory,
probabilistic programming marries Bayesian inference with universal
computation. Probabilistic programming can make it easier to build
useful, fast machine learning software that goes significantly beyond
graphical models in flexibility and power. I will illustrate
probabilistic programming using page-long probabilistic programs that
break simple CAPTCH As— I will also describe stochastic digital circuit architectures that carry these principles down to the physical layer and yield 1000x speed and 10-100x power improvements over deterministic designs on problems of optical flow, clustering, and inference in discrete graphical models. Throughout, I will highlight the ways probabilistic programming points the way to a new model of computation, based on universal inference over distributions rather than universal calculation of functions, and exposes the mathematical and algorithmic structure needed to engineer efficient, distributed machine learning systems. This talk is part of the Machine Learning @ CUED series. ## This talk is included in these lists:- Seminar
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