conditional neural process

Introduction

To train a model effectively, deep neural networks require large datasets. To mitigate this data efficiency problem, learning in two phases is one approach : the first phase learns the statistics of a generic domain without committing to a specific learning task; the second phase learns a function for a specific task, but does so using only a small number of data points by exploiting the domain-wide statistics already learned.

For example, consider a data set [math]\displaystyle{ \{x_i, y_i\} }[/math] for [math]\displaystyle{ i = 0,..., n-1 }[/math] with evaluations [math]\displaystyle{ y_i = f(x_i) }[/math] for some unknown function [math]\displaystyle{ f }[/math]. Assume [math]\displaystyle{ g }[/math] is an approximating function of f. The aim is yo minimize the loss between [math]\displaystyle{ f }[/math] and [math]\displaystyle{ g }[/math] on the entire space [math]\displaystyle{ X }[/math]