Classifying Resources Using a Deep Network

A method performed by one or more computers, the method comprising:

• receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource;
• processing each of the features using a respective embedding function to generate one or more numeric values;
• processing the numeric values using one or more neural network layers to generate an alternative representation of the features of the resource, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values;
• and
• processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category.

A computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource;
• processing each of the features using a respective embedding function to generate one or more numeric values;
• processing the numeric values using one or more neural network layers to generate an alternative representation of the features of the resource, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values;
• and
• processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category.