1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchSebastian Nowozinhttps://www.microsoft.com/en-us/research/people/senowozi/Adversarial Variational Bayes - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="tXzHQt1j0j"><a href="https://www.microsoft.com/en-us/research/publication/adversarial-variational-bayes/">Adversarial Variational Bayes</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/adversarial-variational-bayes/embed/#?secret=tXzHQt1j0j" width="600" height="338" title="“Adversarial Variational Bayes” — Microsoft Research" data-secret="tXzHQt1j0j" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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https://www.microsoft.com/en-us/research/wp-content/uploads/2017/06/avb.png655218Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve […]