2016
PGM
PGM 2016
Online Algorithms for Sum-Product Networks with Continuous Variables
Abstract
Sum-product networks (SPNs) have recently emerged as an attractive representation due to their dual interpretation as a special type of deep neural network with clear semantics and a tractable probabilistic graphical model. We explore online algorithms for parameter learning in SPNs with continuous variables. More specifically, we consider SPNs with Gaussian leaf distributions and show how to derive an online Bayesian moment matching algorithm to learn from streaming data. We compare the resulting generative models to stacked restricted Boltzmann machines and generative moment matching networks on real-world datasets.
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Conference Pioneer
- PGM 2016
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Hot Topic Early Bird
- gaussian distribution
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Bayesian Inference
Deep Learning > Architectures > Neural Networks
Deep Learning > Models > Generative Models
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Learning Types > Online Learning