公开(公告)号：US20250053866A1

公开(公告)日：2025-02-13

申请号：US18618238

申请日：2024-03-27

Applicant: Samsung Electronics Co., Ltd.

Inventor： Subhash Kumar SINGH ,  Sukhdeep SINGH ,  Naman GUPTA ,  Peter Moonki HONG ,  Seungil YOON

IPC: G06N20/00

Abstract: A method for sharing data between machine learning (ML) applications with a network training platform. The method includes: receiving a request to register a first ML application with the network training platform, wherein the request comprises first one or more parameters related to the first ML application; identifying at least one second ML application registered with the network training platform based on the first one or more parameters; identifying second one or more parameters related to the at least one second ML application; comparing the first one or more parameters with the second one or more parameters related to the at least one second ML application; and sharing, with the first ML application, predicted data corresponding to the at least one second ML application based on the comparing.

公开(公告)号：US20250053868A1

公开(公告)日：2025-02-13

申请号：US18752263

申请日：2024-06-24

Applicant: Samsung Electronics Co., Ltd.

Inventor： Sandeep Kumar JAISAWAL ,  Sukhdeep SINGH ,  Joseph THALIATH ,  Seungil YOON ,  Peter Moonki HONG

IPC: G06N20/00

Abstract: Methods for optimizing training of a data driven model in a wireless network by data driven model validation controller running in electronic device. The method may include obtaining and selecting a candidate data driven model from a plurality of candidate data driven models. The method may include determining whether the selected candidate data driven model meets a predefined prediction. The method may include deploying the selected candidate data driven model to a target deployment environment upon determining that the selected candidate data driven model meets the prediction. The method may include sending the selected candidate data driven model to a data driven model optimizer running in the electronic device for tuning a hyper-parameter and the data driven technique running in the data driven model upon determining that the selected candidate data driven model does not meet the prediction.