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31.发明申请公开(公告)号：US20250094460A1
公开(公告)日：2025-03-20
申请号：US18969597
申请日：2024-12-05
Inventor： Haifeng WANG , Hua WU , Hao TIAN , Jing LIU , Ruiqing ZHANG , Yan CHEN , Yu RAN
IPC: G06F16/3329 , G06F16/3332 , G06F16/334
Abstract: A query answering method, an electronic device, a storage medium, and an intelligent agent are provided, which relate to a field of artificial intelligence technology, and in particular to fields of large model, intelligent search and information processing technology. The method includes: inputting, in response to a retrieval content set retrieved based on a query, the query, the retrieval content set and prompt information for answer generation into the large model, so that the large model performs operations of: processing, based on a current task in the prompt information and the query, a current text corresponding to the retrieval content set to obtain a processed text, where the current task is determined based on a task execution order in the prompt information; and obtaining, in a case of determining that the processed text meets a preset condition, an answer to the query based on the processed text.
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公开(公告)号：US20250061305A1
公开(公告)日：2025-02-20
申请号：US18936686
申请日：2024-11-04
Inventor： Shuohuan WANG , Junyuan SHANG , Yinqi YANG , Guoxia WANG , Linhao ZHANG , Yu SUN , Hua WU , Haifeng WANG
IPC: G06N3/043 , G06N3/045 , G06N3/0985
Abstract: A training method, an inference method, a device, an apparatus, and a medium for a deep learning model are provided. A first model includes a plurality of first parameters, a second model comprises a plurality of second parameters, which is initialized to parameter values of a plurality of target parameters selected from the plurality of first parameters. The training method includes: determining a target loss for both the first model and the second model; adjusting parameter values, including: in response to determining that the target loss indicates that the parameter values of at least part of the target parameters need to be adjusted, synchronously adjusting the parameter values of the corresponding second parameters; and in response to determining that the target loss indicates that the parameter values of at least part of the second parameters need to be adjusted, synchronously adjusting the parameter values of the corresponding target parameters.
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公开(公告)号：US20240394190A1
公开(公告)日：2024-11-28
申请号：US18696757
申请日：2022-09-27
Inventor： Minxu ZHANG , Haifeng WANG , Fan ZHANG , Xinxuan WU , Xuefeng YAO , Danlei FENG , Zhihua WU , Zhipeng TAN , Jie DING , Dianhai YU
IPC: G06F12/0873 , G06F12/0815 , G06F15/80
Abstract: The present application provides a method of training a deep learning model. A specific implementation solution of the method of training the deep learning model includes: determining, according to first training data for a current training round, a first target parameter required to be written into a target memory in a first network parameter required by an embedding of the first training data, wherein the target memory is a memory contained in a target processor; determining a remaining storage slot in the target memory according to a first mapping relationship between a storage slot of the target memory and a network parameter; and writing, in response to the remaining storage slot meeting a storage requirement of the first target parameter, the first target parameter into the target memory so that a computing core contained in the target processor adjusts the first network parameter according to the first training data.
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公开(公告)号：US20240185379A1
公开(公告)日：2024-06-06
申请号：US17758692
申请日：2021-11-17
Inventor： Deguo XIA , Jizhou HUANG , Haifeng WANG
Abstract: A method and an apparatus for generation a high definition map, a device and a computer storage medium, which relate to automatic driving and deep learning technologies in the field of artificial intelligence technologies, are disclosed. An implementation includes: acquiring point cloud data and front-view image data which are collected respectively by a plurality of collecting devices at a plurality of location points to obtain a sequence of point clouds and a sequence of front-view images; performing registration of the front-view images and the point clouds on the sequence of point clouds and the sequence of front-view images; transforming the sequence of front-view images into a top-view image based on the result of the registration and determining coordinate information of each pixel in the top-view image; and identifying map elements of the top-view image to obtain the high definition map.
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公开(公告)号：US20230041943A1
公开(公告)日：2023-02-09
申请号：US17961930
申请日：2022-10-07
Inventor： Deguo XIA , Jizhou HUANG , Haifeng WANG
IPC: G01C21/00
Abstract: The present disclosure provides a method and apparatus for automatically producing map data. The method includes: performing track rectification on crowdsourcing tracks based on corresponding standard tracks, and locating each map element included, based on depth information of track point images included in the rectified crowdsourcing tracks; comparing a latest map element obtained based on the rectified crowdsourcing tracks locating and an old map element at a corresponding locating position using a pre-built entity semantic map; determining, in response to a change in the latest map element compared to the old map element, a target processing method according to a processing standard of a changed map element pre-abstracted from a map element update specification; and processing the latest map element according to the target processing method to obtain a processed latest map.
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Patent Agency Ranking
公开(公告)号：US20230032324A1
公开(公告)日：2023-02-02
申请号：US17966127
申请日：2022-10-14
Inventor： Fan WANG , Hao TIAN , Haoyi XIONG , Hua WU , Jingzhou HE , Haifeng WANG
IPC: G06N20/00
Abstract: A method for training a decision-making model parameter, a decision determination method, an electronic device, and a non-transitory computer-readable storage medium are provided. In the method, a perturbation parameter is generated according to a meta-parameter, and first observation information of a primary training environment is acquired based on the perturbation parameter. According to the first observation information, an evaluation parameter of the perturbation parameter is determined. According to the perturbation parameter and the evaluation parameter thereof, an updated meta-parameter is generated. The updated meta-parameter is determined as a target meta-parameter, when it is determined, according to the meta-parameter and the updated meta-parameter, that a condition for stopping primary training is met. According to the target meta-parameter, a target memory parameter corresponding to a secondary training task is determined, where the target memory parameter and the target meta-parameter are used to make a decision corresponding to a prediction task.
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公开(公告)号：US20220391774A1
公开(公告)日：2022-12-08
申请号：US17887951
申请日：2022-08-15
Inventor： Feng XING , Xiang LAN , Liling NIU , Xiandong LIU , Yanjun MA , Dianhai YU , Haifeng WANG
Abstract: A method and apparatus for generating an operator are provided. The method includes: constructing a group of basic application programming interfaces for providing one of the following basic functions: an access function, a storage function, and a computing function; constructing a kernel application programming interface for invoking the basic application programming interfaces to implement an operator logic; and generating a target kernel operator based on the group of basic application programming interfaces and the kernel application programming interface.
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公开(公告)号：US20220391602A1
公开(公告)日：2022-12-08
申请号：US17820765
申请日：2022-08-18
Inventor： Haifeng WANG , Zhanyi LIU , Zhongjun HE , Hua WU , Zhi LI , Xing WAN , Jingxuan ZHAO , Ruiqing ZHANG , Chuanqiang ZHANG , Fengtao HUANG , Hanbing SONG , Wei DI , Shuangshuang CUI , Yongzheng XIN
Abstract: A display method, an electronic device, and a storage medium, which relate to a field of natural language processing and a field of display. The display method includes: acquiring a content to be displayed; extracting a target term from the content using a term extraction rule; acquiring an annotation information for at least one target term, responsive to an extraction of the at least one target term; and displaying the annotation information for the at least one target term and the content.
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公开(公告)号：US20220215899A1
公开(公告)日：2022-07-07
申请号：US17557691
申请日：2021-12-21
Inventor： Fan WANG , Jingzhou HE , Xiaomin FANG , Xiaonan ZHANG , Hua WU , Tian WU , Haifeng WANG
Abstract: The present disclosure discloses an affinity prediction method and apparatus, a method and apparatus for training an affinity prediction model, a device and a medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, smart medical technologies, or the like. An implementation includes: collecting a plurality of training samples, each training sample including information of a training target, information of a training drug and a test data set corresponding to the training target; and training an affinity prediction model using the plurality of training samples. In addition, there is further disclosed the affinity prediction method. The technology in the present disclosure may effectively improve accuracy and a training effect of the trained affinity prediction model. During an affinity prediction, accuracy of a predicted affinity of a target to be detected with a drug to be detected may be higher by acquiring a test data set corresponding to the target to be detected to participate in the prediction.
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公开(公告)号：US20220215180A1
公开(公告)日：2022-07-07
申请号：US17643053
申请日：2021-12-07
Inventor： Jianglu Hu , Hehan LI , Huifeng SUN , Shuqi SUN , Yue CHANG , Tingting LI , Hua WU , Haifeng WANG
IPC: G06F40/35 , G06F16/332
Abstract: The disclosure provides a method for generating a dialogue. The method includes: obtaining an input sentence; determining a type of a task-based response sentence that is to be generated, by updating a current dialogue state based on the input sentence; generating the task-based response sentence by inputting the input sentence into a task-based dialogue response generator; and determining the task-based response sentence as a target response sentence in response to the type of the task-based response sentence being a designated type.
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