Scalable Inference in Hierarchical Generative Models
Thomas Dean · 2006
Borrowing insights from computational neuroscience, we present a family of inference algorithms for a class of generative statistical models specifically designed to run on commonly-available distributed-computing hardware. The class of generative models is roughly based on the architecture of the visual cortex and shares some of the same structural and computational characteristics. In addition to describing several variants of the basic algorithm, we present preliminary experimental results demonstrating the pattern-recognition capabilities of our approach and some of the characteristics of the approximations that the algorithms produce. 1