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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 228-239 |
| Number of pages | 12 |
| Journal | Journal of Machine Learning Research |
| Volume | 52 |
| Issue number | 2016 |
| State | Published - 2016 |
| Externally published | Yes |
| Event | 8th International Conference on Probabilistic Graphical Models, PGM 2016 - Lugano, Switzerland Duration: Sep 6 2016 → Sep 9 2016 |
Keywords
- Continuous variables
- Online learning
- Sum-product networks
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence