公开(公告)号：US12131256B2

公开(公告)日：2024-10-29

申请号：US17237574

申请日：2021-04-22

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Sathyanarayanan Manamohan ,  Patrick Leon Gartenbach ,  Markus Philipp Wuest ,  Krishnaprasad Lingadahalli Shastry ,  Suresh Soundararajan

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04

Abstract: A system and a method for training non-parametric Machine Learning (ML) model instances in a collaborative manner is disclosed. A non-parametric ML model instance is trained at each of a plurality of data processing nodes to obtain a plurality of non-parametric ML model instances. Each non-parametric ML model instance developed at each data processing node is shared with each of remaining data processing nodes of the plurality of data processing nodes. Each non-parametric ML model instance is processed through a trainable parametric combinator to generate a composite model at each of the plurality of data processing nodes. The composite model is trained at each of the plurality of data processing nodes, over the respective local dataset, using Swarm learning to obtain trained composite models.

公开(公告)号：US12229132B2

公开(公告)日：2025-02-18

申请号：US17224832

申请日：2021-04-07

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Krishnamoorthy Balaraman ,  Abhijeet Walimbe ,  Suresh Soundararajan

IPC: G06F16/2453

Abstract: Example techniques for execution of query plans are described. In an example, a query plan may include a first sub-plan and a second sub-plan that are to provide the same output. One among the first sub-plan and the second sub-plan may be selected during execution of the query plan based on a runtime performance parameter of a component to be involved in execution of the first sub-plan and a runtime performance parameter of a component to be involved in execution of the second sub-plan.