公开(公告)号：US20200242450A1

公开(公告)日：2020-07-30

申请号：US16850549

申请日：2020-04-16

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Ruiming TANG ,  Minzhe NIU ,  Yanru QU ,  Weinan ZHANG ,  Yong YU

IPC: G06N3/04 ,  G06N3/063 ,  G06K9/62 ,  G06F17/16

Abstract: Example user behavior prediction methods and apparatus are described. One example method includes obtaining a first contribution value of each piece of characteristic data for a specified behavior after obtaining behavior prediction information including a plurality of pieces of characteristic data. Every N pieces of characteristic data in the plurality of pieces of characteristic data may be processed by using one corresponding characteristic interaction model, to obtain a second contribution value of the every N pieces of characteristic data for the specified behavior. Finally, an execution probability of executing the specified behavior by a user may be determined based on the obtained first contribution value and the obtained second contribution value, to predict a user behavior. In the example method, interaction impact of the plurality of pieces of characteristic data on the specified behavior is considered during behavior prediction.

公开(公告)号：US20230141145A1

公开(公告)日：2023-05-11

申请号：US18150748

申请日：2023-01-05

Applicant: Huawei Technologies Co., Ltd.

Inventor： Weijun HONG ,  Guilin LI ,  Weinan ZHANG ,  Yong YU ,  Xing ZHANG ,  Zhenguo LI

IPC: G06N3/04 ,  G06N3/082

CPC classification number: G06N3/04 ,  G06N3/082

Abstract: A neural network building method and apparatus are disclosed, and relate to the field of artificial intelligence. The method includes: initializing a search space and a plurality of building blocks, where the search space includes a plurality of operators, and the building block is a network structure obtained by connecting a plurality of nodes by using the operator; during training, in at least one training round, randomly discarding some operators, and updating the plurality of building blocks by using operators that are not discarded; and building a target neural network based on the plurality of updated building blocks. In the method, some operators are randomly discarded. This breaks association between operators, and overcomes a co-adaptation problem during training, to obtain a target neural network with better performance.