Auto-Encoding Variational Bayes · HackerPapers discussions

Auto-Encoding Variational Bayes — mentioned by xcodevn

Sun, 14 Oct 2018 16:34:30 GMT

I truly believe that Bayesian inference is the statistics of the 21st century. Recent advances in MCMC (e.g., NUTS, Stan [1]) and variational inference (e.g., ADVI [2], VAE [3], etc.) + more computing power than ever promise a near future in which Bayesian inference is the default inference engine. Prior distribution is a beautiful and logical mechanism for adding regularization, domain-specific knowledge to our model. [1] Stan, a platform for statistical modeling http://mc-stan.org/ [2] Automatic Differentiation Variational Inference https://arxiv.org/abs/1603.00788 [3] Auto-Encoding…

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Auto-Encoding Variational Bayes — mentioned by aqsalose

Tue, 03 Oct 2017 18:04:22 GMT

This is the PhD thesis of Durk Kingma [1]. Reading this right now; appears to be a well-written introduction to stuff variational autoencoders [2] and ADAM [3]. [1] http://dpkingma.com/ [2] http://arxiv.org/abs/1312.6114 [3] http://arxiv.org/abs/1412.6980

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Auto-Encoding Variational Bayes — mentioned by tabacof

Wed, 02 Sep 2015 20:15:07 GMT

Causality is a bit harder to integrate with current machine learning models as it's hard even with standard probabilistic graphical models. On the other hand, there has been a lot of work integrating deep neural networks with probabilistic models. For example, the variational auto-encoders are a graphical model with Gaussian latent variables whose mean and variance are determined by (deep) neural networks [1]. There has been work exploring the neural network weights as latent variables themselves [2]. Finally, some new developments such as dropout can be interpreted as some form of deep…

Paper and discussion history. A mention is not an endorsement.