authored by (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


General Resources ✨

Book Chapter:

Presentation Script/Notes

Next Up - Methods 🚶


MAP / Sparse Encoding

Variational Inference and Learning

Sampling based methods


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

(unevaluated, but interesting) Adversarially learned inference

[1606.00704] Adversarially Learned Inference

New paper argung that SGD implicitly performs variational inference