Patent ID: 11915120
Assignee: GOOGLE LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. A computing system, comprising:
at least one processor;
a multi-task machine-learned model configured to perform a plurality of tasks T, comprising:
a plurality of layers L, each layer comprising a plurality of components C;
a routing matrix of size T×C associated with each respective layer, the routing matrix for a particular layer comprising a matrix of binary allocation variables descriptive of which components in the respective layer an input into the machine-learned model is routed through to generate an output, wherein a respective binary allocation variable of the routing matrix was obtained by sampling, using a respective probability value associated with a respective component and a respective task of the plurality of tasks, the respective value of the routing matrix; and
a plurality of task-specific heads, each task-specific head configured to receive an output from a final layer of the one or more layers and generate an output associated with a respective task; and

at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
obtaining an input;
selecting a particular task and binary allocation variables corresponding to that particular task;
routing the input through the machine-learned model according to the respective routing matrix for each respective layer for the particular task; and
receiving, as an output of the machine-learned model, a task-specific output from the task-specific head associated with the particular task;

wherein the multi-task machine-learned model has been trained to jointly learn the routing matrix with the plurality of components using back-propagation.