authored by mimee@unify.id (prev. @Baidu Silicon Valley AI Lab), Oct 23, 2017


Core Ideas ✨


Inference in a nutshell:

Computing posterior distribution

$$p(\vec{h}|\vec{v})$$

where v is observed data, and h is latent.

Here, we (mostly) see inference as optimization by augmenting p with a distribution q on latent h

ELBO

General Resources ✨


Book Chapter:

Presentation Script/Notes

Next Up - Methods 🚶


E-M

MAP / Sparse Encoding

Variational Inference and Learning

Sampling based methods

Wake-Sleep

Stretch goal reading 🏃


Marginal Likelihood (what we are trying to bound in the very beginning)

Marginal likelihood - Wikipedia

E-M intro (behind paywall :( )

What is the expectation maximization algorithm?

Auto-encoding variational bayes

https://www.youtube.com/watch?v=rjZL7aguLAs

(unevaluated, but interesting) Adversarially learned inference

[1606.00704] Adversarially Learned Inference

New paper argung that SGD implicitly performs variational inference