Adversarial network systems and methods

A method comprising:

• receiving data for a plurality of individual users from a plurality of data sources;
• determining a user's signals from the data;
• for each data source, combining the user's signals to create a user vector of a first fixed size;
• transforming the user vector by a neural network into a vector of a second fixed size, the neural network trained a) by determining a classifier loss, a discriminator loss and an encoder loss, and b) by adjusting the weights of the network to change the losses;
• receiving a query;
• and
• providing information in response to the query based on at least in part on the vector of the second fixed size.

A system comprising:

• one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
• receiving, at each encoder of a plurality of encoders, individual user datasets for a plurality of individual users from a single specified source, the plurality of encoders receiving data from a plurality of data sources;
• generating a plurality of vectors, wherein generating a plurality of vectors comprises, for each individual user dataset, generating a vector of a specified size, the plurality of vectors forming a shared representation and wherein each of the plurality of encoders comprises a machine learning model trained based at least in part on:
• a) an encoder's loss, b) a classifier loss, and c) a discriminator loss that is based at least in part on vectors received from at least some of the plurality of encoders;
• receiving a query of the shared representation;
• and
• providing information from the shared representation in response to the query.