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Summary by Artëm Sobolev 6 years ago
If one is a Bayesian he or she best expresses beliefs about next observation $x_{n+1}$ after observing $x_1, \dots, x_n$ using the **posterior predictive distribution**: $p(x_{n+1}\vert x_1, \dots, x_n)$. Typically one invokes the de Finetti theorem and assumes there exists an underlying model $p(x\vert\theta)$, hence $p(x_{n+1}\vert x_1, \dots, x_n) = \int p(x_{n+1} \vert \theta) p(\theta \vert x_1, \dots, x_n) d\theta$, however this integral is far from tractable in most cases. Nevertheless, having tractable posterior predictive is useful in cases like few-shot generative learning where we only observe a few instances of a given class and are asked to produce more of it.
In this paper authors take a slightly different approach and build a neural model with tractable posterior predictive distribution $p(x_{n+1} | x_1, \dots, x_n)$ suited for complex objects like images. In order to do so the authors take a simple model with tractable posterior predictive $p(z_{n+1} | z_1, \dots, z_n)$ (like a Gaussian Process, but not quite) and use it as a latent code, which is obtained from observations using an analytically inversible encoder $f$. This setup lets you take a complex $x$ like an image, run it through $f$ to obtain $z = f(x)$ -- a simplified latent representation for which it's easier to build joint density of all possible representations and hence easier to model the posterior predictive. By feeding latent representations of $x_1, \dots, x_n$ (namely, $z_1, \dots, z_n$) to the posterior predictive $p(z_{n+1} | f(x_1), \dots, f(x_n))$ we obtain obtain a distribution of latent representations that are coherent with those of already observed $x$s. By sampling $z$ from this distribution and running it through $f^{-1}$ we recover an object in the observation space, $x_\text{pred} = f^{-1}(z)$ -- a sample most coherent with previous observations.
Important choices are:
* Model for latent representations $z$: one could use Gaussian Process, however authors claim it lacks some helpful properties and go for a more general [Student-T Process](http://www.shortscience.org/paper?bibtexKey=journals/corr/1402.4306). Then authors assume that each component of $z$ is a univariate sample from this process (and hence is independent from other components)
* Encoder $f$. It has to be easily inversible and have an easy-to-evaluate Jacobian (the determinant of the Jacobi matrix). The former is needed to perform decoding of predictions in latent representations space and the later is used to efficiently compute a density of observations $p(x_1, \dots, x_n)$ using the standard change of variables formula $$p(x_1, \dots, x_n) = p(z_1, \dots, z_n) \left\vert\text{det} \frac{\partial f(x)}{\partial x} \right\vert$$The architecture of choice for this task is [RealNVP](http://www.shortscience.org/paper?bibtexKey=journals/corr/1605.08803)
Done this way, it's possible to write out the marginal density $p(x_1, \dots, x_n)$ on all the observed $x$s and maximize it (as in the Maximum Likelihood Estimation). Authors choose to factor the joint density in an auto-regressive fashion (via the chain rule) $$p(x_1, \dots, x_n) = p(x_1) p(x_2 \vert x_1) p(x_3 \vert x_1, x_2) \dots p(x_n \vert x_1, \dots, x_{n-1}) $$with all the conditional marginals $p(x_i \vert x_1, \dots, x_{i-1})$ having analytic (student t times the jacobian) density -- this allows one to form a fully differentiable recurrent computation graph whose parameters (parameters of Student Processes for each component of $z$ + parameters of the encoder $f$) to be learned using any stochastic gradient method.
https://i.imgur.com/yRrRaMs.png

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