arXiv Open Access 2016

Note on the equivalence of hierarchical variational models and auxiliary deep generative models

Niko Brümmer

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Abstrak

This note compares two recently published machine learning methods for constructing flexible, but tractable families of variational hidden-variable posteriors. The first method, called "hierarchical variational models" enriches the inference model with an extra variable, while the other, called "auxiliary deep generative models", enriches the generative model instead. We conclude that the two methods are mathematically equivalent.

Topik & Kata Kunci

stat.ML

Penulis (1)

Niko Brümmer

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APA MLA BibTeX

Brümmer, N. (2016). Note on the equivalence of hierarchical variational models and auxiliary deep generative models. https://arxiv.org/abs/1603.02443

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Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓