USING EMBEDDING FUNCTIONS WITH A DEEP NETWORK: abstract simplified (17972466)

• This abstract for appeared for patent application number 17972466 Titled 'USING EMBEDDING FUNCTIONS WITH A DEEP NETWORK'

Simplified Explanation

The abstract describes a method for using embedded function with a deep network. This method involves receiving input with different types of features, processing each feature using a specific embedding function to generate numeric values, and then using a deep network to generate an alternative representation of the input. Finally, a logistic regression classifier is used to predict a label for the input based on the alternative representation.

Original Abstract Submitted

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.