公开(公告)号：US20230031522A1

公开(公告)日：2023-02-02

申请号：US17964117

申请日：2022-10-12

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Bin Liu ,  Ruiming Tang ,  Huifeng Guo ,  Niannan Xue ,  Guilin Li ,  Xiuqiang He ,  Zhenguo Li

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: This application relates to the field of artificial intelligence. A recommendation method based on automatic feature grouping includes: obtaining a plurality of candidate recommended objects and a plurality of association features of each of the plurality of candidate recommended objects; performing multi-order automatic feature grouping on the plurality of association features of each candidate recommended object, to obtain a multi-order feature interaction set of each candidate recommended object; obtaining an interaction feature contribution value of each candidate recommended object through calculation based on the plurality of association features in the multi-order feature interaction set of each candidate recommended object; obtaining a prediction score of each candidate recommended object through calculation based on the interaction feature contribution value of each candidate recommended object; and determining one or more corresponding candidate recommended objects with a high prediction score as a target recommended object.

公开(公告)号：US20230206069A1

公开(公告)日：2023-06-29

申请号：US18175936

申请日：2023-02-28

Applicant: Huawei Technologies Co., Ltd.

Inventor： Junlei Zhang ,  Chuanjian Liu ,  Guilin Li ,  Xing Zhang ,  Wei Zhang ,  Zhenguo Li

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: A deep learning training method includes obtaining a training set, a first neural network, and a second neural network, where shortcut connections included in the first neural network are less than shortcut connections included in the second neural network; performing at least one time of iterative training on the first neural network based on the training set, to obtain a trained first neural network, where any iterative training includes: using a first output of at least one first intermediate layer in the first neural network as an input of at least one network layer in the second neural network, to obtain an output result of the at least one network layer; and updating the first neural network according to a first loss function.

公开(公告)号：US20230082597A1

公开(公告)日：2023-03-16

申请号：US17990125

申请日：2022-11-18

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yunfeng Lin ,  Guilin Li ,  Xing Zhang ,  Weinan Zhang ,  Zhenguo Li

IPC: G06N3/04 ,  G06N3/08

Abstract: A neural network construction method and system in the field of artificial intelligence, to construct a target neural network by replacing a part of basic units in an initial backbone network with placeholder modules, so that different target neural networks can be constructed based on different scenarios. The method may include obtaining an initial backbone network and a candidate set, replacing at least one basic unit in the initial backbone network with at least one placeholder module to obtain a to-be-determined network, performing sampling based on the candidate set to obtain information about at least one sampling structure, and obtaining a network model based on the to-be-determined network and the information about the at least one sampling structure. The information about the at least one sampling structure may be used for determining a structure of the at least one placeholder module.

公开(公告)号：US20230026322A1

公开(公告)日：2023-01-26

申请号：US17948392

申请日：2022-09-20

Applicant: Huawei Technologies Co., Ltd.

Inventor： Guilin Li ,  Bin Liu ,  Ruiming Tang ,  Xiuqiang He ,  Zhenguo Li

IPC: G06N3/08 ,  G06N3/04

Abstract: A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.