公开(公告)号：US20240104403A1

公开(公告)日：2024-03-28

申请号：US18521061

申请日：2023-11-28

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Yaqing Wang ,  Hongming Piao ,  Longteng Xu ,  Daxiang Dong ,  Jingbo Zhou

IPC: G06N5/022

CPC classification number: G06N5/022

Abstract: A method for training a click rate prediction model includes: obtaining sample feature information and a label value, in which the sample feature information includes feature information of a sample user and feature information of a target object, and the label value is configured to indicate whether the sample user interacts with the target object; obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module; obtaining a click rate prediction value of the sample user on the target object using the prediction module, according to the sample feature information and the plurality of adjacent matrixes; and training the click rate prediction model according to the label value and the click rate prediction value.

公开(公告)号：US20220036241A1

公开(公告)日：2022-02-03

申请号：US17501003

申请日：2021-10-14

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Tianjian He ,  Dianhai Yu ,  Zhihua Wu ,  Daxiang Dong ,  Yanjun Ma

IPC: G06N20/00 ,  G06F9/48 ,  G06F9/52

Abstract: The present disclosure discloses a method, an apparatus and a storage medium for training a deep learning framework, and relates to the artificial intelligence field such as deep learning and big data processing. The specific implementation solution is: acquiring at least one task node in a current task node cluster, that meets a preset opening condition when a target task meets a training start condition; judging whether a number of nodes of the at least one task node is greater than or equal to a preset number; synchronously training the deep learning framework of the target task by the at least one task node according to sample data if the number of nodes is greater than the preset number; and acquiring a synchronously trained target deep learning framework when the target task meets a training completion condition.