公开(公告)号：US20240143977A1

公开(公告)日：2024-05-02

申请号：US18496177

申请日：2023-10-27

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Zhiyuan ZHANG ,  Xuancheng REN ,  Xu SUN ,  Bin HE ,  Li QIAN

IPC: G06N3/045 ,  G06N3/084

CPC classification number: G06N3/045 ,  G06N3/084

Abstract: This application discloses a model training method, which may be applied to the field of artificial intelligence. The method includes: when training a first neural network model based on a training sample, determining N parameters from M parameters of the first neural network model based on a capability of affecting data processing precision by each parameter; and updating the N parameters. In this application, on a premise that it is ensured that the data processing precision of the model meets a precision requirement, because only N parameters in M parameters in an updated first neural network model are updated, an amount of data transmitted from a training device to a terminal device can be reduced.

公开(公告)号：US20240104346A1

公开(公告)日：2024-03-28

申请号：US17945978

申请日：2022-09-15

Applicant: Huawei Technologies Co., Ltd.

Inventor： Lu HOU ,  Chaofan TAO ,  Wei ZHANG ,  Lifeng SHANG ,  Xin JIANG ,  Qun LIU ,  Li QIAN

IPC: G06N3/04

CPC classification number: G06N3/0454

Abstract: A method is provided for quantizing a neural network model performed by a processing system. The method comprises determining a scaling factor based on a distribution of weights associated with the neural network model, determining quantized weights based on the scaling factor and the weights associated with the distribution, determining a training loss of the neural network model based on the quantized weights during training of the neural network model, and determining an updated scaling factor for the neural network model based on a gradient of the training loss.

公开(公告)号：US20230237333A1

公开(公告)日：2023-07-27

申请号：US18185550

申请日：2023-03-17

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yunfeng SHAO ,  Shaoming SONG ,  Wenpeng LI ,  Kaiyang GUO ,  Li QIAN

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: A machine learning model training method is applied to a first client, a plurality of clients are communicatively connected to a server, the server stores a plurality of modules, and the plurality of modules are configured to construct at least two machine learning models. The method includes: obtaining a first machine learning model, where at least one first machine learning model is selected based on a data feature of a first training data set stored in the first client; performing a training operation on the at least one first machine learning model by using the first data set, to obtain at least one trained first machine learning model; and sending at least one updated module to the server, where the updated module is used by the server to update weight parameters of the stored modules.

公开(公告)号：US20220214894A1

公开(公告)日：2022-07-07

申请号：US17701339

申请日：2022-03-22

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Tao CAI ,  Lifeng SHANG ,  Xiaoguang LI ,  Yuyang ZHANG ,  Wei ZHANG ,  Li QIAN

IPC: G06F9/451 ,  G06F3/0481 ,  G06F3/0487

Abstract: Embodiments of the present disclosure disclose a command execution method and apparatus, a terminal, and a server related speech recognition and natural language processing. In the command execution method, during an interaction between a terminal and a user, a server configured to execute a user command or the terminal may store slots and GUI information corresponding to the slots. When the filling information of the slots configured for the user command is missing, the server configured to execute the user command may obtain the missing filling information of the slots from the stored GUI information, to avoid multiple interactions between the user and the terminal. The interaction between the user and the terminal is made more intelligent, thus improving command execution efficiency.

公开(公告)号：US20220092351A1

公开(公告)日：2022-03-24

申请号：US17538640

申请日：2021-11-30

Applicant: Huawei Technologies Co., Ltd. ,  PEKING UNIVERSITY

Inventor： Weiran HUANG ,  Aoxue LI ,  Zhenguo LI ,  Tiange LUO ,  Li QIAN ,  Liwei WANG

IPC: G06K9/62 ,  G06K9/00 ,  G06K9/46 ,  G06N3/08

Abstract: An image classification method, a neural network training method, and an apparatus are provided, and relate to the field of artificial intelligence, and specifically, to the field of computer vision. The image classification method includes: obtaining a to-be-processed image; and obtaining a classification result of the to-be-processed image based on a pre-trained neural network model, where the classification result includes a class or a superclass to which the to-be-processed image belongs. When the neural network model is trained, not only labels of a plurality of training images but also class hierarchy information of the plurality of training images is used. That is, more abundant information of the training images is used. Therefore, images can be better classified.

公开(公告)号：US20200258006A1

公开(公告)日：2020-08-13

申请号：US16863110

申请日：2020-04-30

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Fei CHEN ,  Zhenhua DONG ,  Zhenguo LI ,  Xiuqiang HE ,  Li QIAN ,  Shuaihua PENG

IPC: G06N20/00 ,  G06N5/02

Abstract: Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

公开(公告)号：US20240152770A1

公开(公告)日：2024-05-09

申请号：US18411616

申请日：2024-01-12

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hang XU ,  Xiaozhe REN ,  Yichun YIN ,  Li QIAN ,  Zhenguo LI ,  Xin JIANG ,  Jiahui GAO

IPC: G06N3/0985 ,  G06N3/04

CPC classification number: G06N3/0985 ,  G06N3/04

Abstract: This application relates to the artificial intelligence field, and discloses a neural network search method and a related apparatus. The neural network search method includes: constructing attention heads in transformer layers by sampling a plurality of candidate operators during model search, to construct a plurality of candidate neural networks, and comparing performance of the plurality of candidate neural networks to select a target neural network with higher performance. In this application, a transformer model is constructed with reference to model search, so that a new attention structure with better performance than an original self-attention mechanism can be generated, and effect in a wide range of downstream tasks is significantly improved.

公开(公告)号：US20210166693A1

公开(公告)日：2021-06-03

申请号：US17171166

申请日：2021-02-09

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yingtao LI ,  Xin JIANG ,  Xiao CHEN ,  Baofeng ZHANG ,  Li QIAN

IPC: G10L15/22 ,  G06N3/04 ,  G06F40/35 ,  G06Q10/02 ,  G06Q10/04

Abstract: The application relates to the field of man-machine interaction in artificial intelligence and provides a multi-task processing method. The method includes the following operations: determining a first task based on request information entered by a user; obtaining key information corresponding to the first task and executing the first task, where the key information includes one or more slots and values of the one or more slots; storing task status information of the first task, where the task status information includes the key information; and predicting and initiating a second task based on the task status information of the first task. A man-machine interaction system may predict a next task based on the stored task status information, and actively initiate the predicted task. This improves intelligence and efficiency of multi-task processing by the man-machine interaction system.

公开(公告)号：US20210096681A1

公开(公告)日：2021-04-01

申请号：US17122620

申请日：2020-12-15

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Li QIAN

IPC: G06F3/041 ,  G06F3/0488

Abstract: Embodiments of the present invention disclose a touchscreen device, and a method and an apparatus for performing an operation that relate to the field of information technologies, so as to reduce a limitation of identifiable operations provided to a user, and improve user experience. The method includes: detecting, by a touchscreen device, pressing force track information of a user on a touchscreen, where the pressing force track information is used to represent a change of a pressing force level in a process in which the user continuously presses the touchscreen; determining an operation corresponding to the pressing force track information, according to a current touch operation application scenario and correspondences between pressing force track information and operations; and performing the operation. The present invention is applicable to a touchscreen device that determines a corresponding operation according to pressing force track information of a user and performs the operation.

公开(公告)号：US20190205348A1

公开(公告)日：2019-07-04

申请号：US16292992

申请日：2019-03-05

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Dawei HUO ,  Li QIAN ,  Xin JIANG

IPC: G06F16/9532 ,  G06F16/9032 ,  G06F17/18 ,  G06F16/9538

CPC classification number: G06F16/9532 ,  G06F16/90332 ,  G06F16/9538 ,  G06F17/18 ,  H04L29/06

Abstract: The present invention discloses a method and an apparatus for sending a search request. The method includes: during a running procedure of a search engine client, generating a forged search request, where the forged search request carries a forged search word; and sending the forged search request to the search engine server. The forged search request is sent to the search engine server, to serve as a factor interfering with an analysis of a user behavior by the search engine server based on a true search request, to prevent the search engine server from analyzing the user behavior based on a search word entered by a user, thereby improving user experience. It is avoided that, in the prior art, a search engine server analyzes a user behavior based on a search word entered by a user.