2,391 research outputs found
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
The benefits of automating design cycles for Bayesian inference-based
algorithms are becoming increasingly recognized by the machine learning
community. As a result, interest in probabilistic programming frameworks has
much increased over the past few years. This paper explores a specific
probabilistic programming paradigm, namely message passing in Forney-style
factor graphs (FFGs), in the context of automated design of efficient Bayesian
signal processing algorithms. To this end, we developed "ForneyLab"
(https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message
passing-based inference in FFGs. We show by example how ForneyLab enables
automatic derivation of Bayesian signal processing algorithms, including
algorithms for parameter estimation and model comparison. Crucially, due to the
modular makeup of the FFG framework, both the model specification and inference
methods are readily extensible in ForneyLab. In order to test this framework,
we compared variational message passing as implemented by ForneyLab with
automatic differentiation variational inference (ADVI) and Monte Carlo methods
as implemented by state-of-the-art tools "Edward" and "Stan". In terms of
performance, extensibility and stability issues, ForneyLab appears to enjoy an
edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate
Reasonin
Scalable variational Gaussian process classification
Gaussian process classification is a popular method with a number of
appealing properties. We show how to scale the model within a variational
inducing point framework, outperforming the state of the art on benchmark
datasets. Importantly, the variational formulation can be exploited to allow
classification in problems with millions of data points, as we demonstrate in
experiments.JH was supported by a MRC fellowship, AM and ZG by EPSRC grant EP/I036575/1, and a Google Focussed Research award.This is the final version of the article. It was first available from JMLR via http://jmlr.org/proceedings/papers/v38/hensman15.pd
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