# Difference between revisions of "conditional neural process"

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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. | 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 display="inline"> {x_i, y_i} </math> for <math display="inline">i = 0 | + | For example, consider a data set <math display="inline"> \{x_i, y_i\} </math> for <math display="inline">i = 0,..., n-1</math> |

## Revision as of 17:34, 18 November 2018

## 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] \{x_i, y_i\} [/math] for [math]i = 0,..., n-1[/math]