公开(公告)号：US11537949B2

公开(公告)日：2022-12-27

申请号：US16871527

申请日：2020-05-11

Applicant: Google LLC

Inventor： Dami Choi ,  Alexandre Tachard Passos ,  Christopher James Shallue ,  George Edward Dahl

IPC: G06N20/00 ,  G06N3/08

Abstract: A method for reducing idleness in a machine-learning training system can include performing operations by computing devices. A first set of training operations can access and prepare a plurality of training examples of a set of training data. A second set of training operations can train a machine-learned model based at least in part on the set of training data and can include one or more repeat iterations in which at least a portion of the second set of training operations is repeatedly performed such that the training example(s) are repeatedly used to train the machine-learned model. A rate of the repeat iteration(s) can be based at least in part on an echo factor that can be based at least in part on a comparison of a first computational time of the first set of training operations to a second computational time of the second set of training operations.

公开(公告)号：US20200372407A1

公开(公告)日：2020-11-26

申请号：US16871527

申请日：2020-05-11

Applicant: Google LLC

Inventor： Dami Choi ,  Alexandre Tachard Passos ,  Christopher James Shallue ,  George Edward Dahl

IPC: G06N20/00

Abstract: A method for reducing idleness in a machine-learning training system can include performing operations by computing devices. A first set of training operations can access and prepare a plurality of training examples of a set of training data. A second set of training operations can train a machine-learned model based at least in part on the set of training data and can include one or more repeat iterations in which at least a portion of the second set of training operations is repeatedly performed such that the training example(s) are repeatedly used to train the machine-learned model. A rate of the repeat iteration(s) can be based at least in part on an echo factor that can be based at least in part on a comparison of a first computational time of the first set of training operations to a second computational time of the second set of training operations.