Sylvester Normalizing Flows for Variational Inference
| Authors | |
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| Publication date | 2018 |
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| Book title | Uncertainty in Artificial Intelligence |
| Book subtitle | proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA |
| ISBN (electronic) |
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| Event | 34th Conference on Uncertainty in Artificial Intelligence |
| Pages (from-to) | 393-402 |
| Publisher | Corvallis, Oregon: AUAI Press |
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| Abstract |
Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets. |
| Document type | Conference contribution |
| Note | With supplementary material |
| Language | English |
| Published at | http://auai.org/uai2018/proceedings/papers/156.pdf http://auai.org/uai2018/proceedings/uai2018proceedings.pdf |
| Other links | http://auai.org/uai2018/proceedings/supplements/Supplementary-Paper156.pdf |
| Downloads |
156
(Accepted author manuscript)
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| Supplementary materials | |
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