Data processing apparatus for accessing shared memory in processing structured data for modifying a parameter vector data structure

A method performed by a data processing apparatus for training a machine learning model to improve the machine learning model's capability to predict a risk-level associated with an entity, the method comprising:

• obtaining a set of training data that includes a plurality of training data structures that each represents attributes of an entity, wherein each training data structure in the set of training data structures represents (i) a plurality of features that are derived from a first set of categories defined by a first model and from a second set of categories defined by a second model, and (ii) a risk-level associated with the entity;
• and
• for each respective training data structure of the plurality of training data structures:
• providing the training data structure as an input to the machine learning model;
• receiving an output from the machine learning model based on the machine learning model's processing of the training data structure;
• determining an amount of error between the output of the machine learning model and the risk-level of the training data structure;
• and
• adjusting one or more parameter values of the machine learning model based on the determined error.

A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by one or more computers, to cause the one or more computers to perform one or more operations, the operations comprising:

• obtaining a set of training data that includes a plurality of training data structures that each represents attributes of an entity, wherein each training data structure in the set of training data structures represents (i) a plurality of features that are derived from a first set of categories defined by a first model and from a second set of categories defined by a second model, and (ii) a risk-level associated with the entity;
• and
• for each respective training data structure of the plurality of training data structures:
• providing the training data structure as an input to the machine learning model;
• receiving an output from the machine learning model based on the machine learning model's processing of the training data structure;
• determining an amount of error between the output of the machine learning model and the risk-level of the training data structure;
• and
• adjusting one or more parameter values of the machine learning model based on the determined error.

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

• obtaining a set of training data that includes a plurality of training data structures that each represents attributes of an entity, wherein each training data structure in the set of training data structures represents (i) a plurality of features that are derived from a first set of categories defined by a first model and from a second set of categories defined by a second model, and (ii) a risk-level associated with the entity;
• and
• for each respective training data structure of the plurality of training data structures:
• providing the training data structure as an input to the machine learning model;
• receiving an output from the machine learning model based on the machine learning model's processing of the training data structure;
• determining an amount of error between the output of the machine learning model and the risk-level of the training data structure;
• and
• adjusting one or more parameter values of the machine learning model based on the determined error.