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Guy Steele

2 accepted papers

2016

Exponential Stochastic Cellular Automata for Massively Parallel Inference

AISTATS 2016poster

We propose an embarrassingly parallel, memory efficient inference algorithm for latent variable models in which the complete data likelihood is in the exponential family. The algorithm is a stochastic cellular automaton and converges to a valid maximum a posteriori fixed point. Applied to latent Dir…

Cited by 30SourcePDFScholar
2015

Efficient Training of LDA on a GPU by Mean-for-Mode Estimation

ICML 2015poster

We introduce Mean-for-Mode estimation, a variant of an uncollapsed Gibbs sampler that we use to train LDA on a GPU. The algorithm combines benefits of both uncollapsed and collapsed Gibbs samplers. Like a collapsed Gibbs sampler — and unlike an uncollapsed Gibbs sampler — it has good statistical per…

Cited by 18SourcePDFScholar