公开(公告)号：US11468327B2

公开(公告)日：2022-10-11

申请号：US16883315

申请日：2020-05-26

Applicant: GE Precision Healthcare LLC

Inventor： Chiranjib Sur ,  Venkata Ratnam Saripalli ,  Gopal B. Avinash

IPC: G06N3/08 ,  G16H50/20 ,  G16H30/40 ,  G06N3/04 ,  G06T7/00

Abstract: A computer-implemented system is provided that includes a learning network component that determines respective weights assigned to respective node inputs of the learning network in accordance with a learning phase of the learning network and trains a variable separator component to differentially change learning rates of the learning network component. A differential rate component applies at least one update learning rate to adjust at least one weight assigned to at least one of the respective node inputs and applies at least one other update learning rate to adjust the respective weight assigned to at least one other of the respective node inputs in accordance with the variable separator component during the learning phase of the learning network. A differential rate component applies at least one update rate to adjust at least one weight assigned to at least one of the respective node inputs and applies at least one other update rate to adjust the respective weight assigned to at least one other of the respective node inputs in accordance with the learning phase of the learning network.

公开(公告)号：US20210374513A1

公开(公告)日：2021-12-02

申请号：US16883315

申请日：2020-05-26

Applicant: GE Precision Healthcare LLC

Inventor： Chiranjib Sur ,  Venkata Ratnam Saripalli ,  Gopal B. Avinash

IPC: G06N3/08 ,  G06N3/04 ,  G16H30/40 ,  G16H50/20 ,  G06T7/00

Abstract: A computer-implemented system is provided that includes a learning network component that determines respective weights assigned to respective node inputs of the learning network in accordance with a learning phase of the learning network and trains a variable separator component to differentially change learning rates of the learning network component. A differential rate component applies at least one update learning rate to adjust at least one weight assigned to at least one of the respective node inputs and applies at least one other update learning rate to adjust the respective weight assigned to at least one other of the respective node inputs in accordance with the variable separator component during the learning phase of the learning network. A differential rate component applies at least one update rate to adjust at least one weight assigned to at least one of the respective node inputs and applies at least one other update rate to adjust the respective weight assigned to at least one other of the respective node inputs in accordance with the learning phase of the learning network.