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公开(公告)号：US20250005446A1
公开(公告)日：2025-01-02
申请号：US18547090
申请日：2022-11-02
Inventor： Weihang CHEN , Haifeng WANG , Yunfei ZHANG , Risheng YUAN , Tianyu CHEN , Hongyu LIU , Xiaoguang HU , Dianhai YU , Yanjun MA
IPC: G06N20/00
Abstract: An operator processing method of a deep learning framework an electronic device, and a storage medium are provided, which relate to a field of computer technology, especially in a field of artificial intelligence technology such as deep learning. The specific implementation scheme includes: acquiring an operator to be processed, where the operator to be processed includes a template parameter independent of the deep learning framework and an operator kernel function; parsing, in response to receiving an input information for the operator to be processed, the template parameter by using the input information to obtain a plurality of complete template parameters related to the deep learning framework; and processing the operator kernel function according to the plurality of complete template parameters, to obtain an available operator for the deep learning framework.
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公开(公告)号：US20240412002A1
公开(公告)日：2024-12-12
申请号：US18747641
申请日：2024-06-19
Inventor： Yanbin ZHAO , Siyu DING , Shuohuan WANG , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG
IPC: G06F40/35
Abstract: A method is provided. The method includes: obtaining a first sample dataset; inputting at least one first question text corresponding to at least one piece of first sample data into a dialog model separately to obtain at least one first answer prediction result; inputting each second question text into the dialog model to obtain a second answer prediction result output by the dialog model; inputting the second answer prediction result into a reward model to obtain a score of the second answer prediction result output by the reward model; determining a comprehensive loss based on the at least one first answer prediction result, a first answer text of each of the at least one piece of first sample data, and a score corresponding to each of at least one piece of second sample data; and adjusting at least one parameter of the dialog model based on the comprehensive loss.
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公开(公告)号：US20230243661A1
公开(公告)日：2023-08-03
申请号：US17999917
申请日：2022-04-28
Inventor： Jizhou HUANG , Kejiao LI , Bo ZHOU , Fan WANG , Jingzhou HE , Haifeng WANG
CPC classification number: G01C21/3461 , G01C21/3492 , G08G1/0108
Abstract: Provided are a navigation path planning method and apparatus, a device, and a storage medium. The navigation path planning method includes planning at least two available navigation paths for each target user of at least two target users in a target region; and determining a global passing feature of the target region and selecting, according to the global passing feature of the target region, one available navigation path from the at least two available navigation paths corresponding to each target user to serve as a recommended navigation path to be recommended to each target user.
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34.发明公开公开(公告)号：US20230215203A1
公开(公告)日：2023-07-06
申请号：US18168759
申请日：2023-02-14
Inventor： Pengyuan LV , Chengquan ZHANG , Shanshan LIU , Meina QIAO , Yangliu XU , Liang WU , Xiaoyan WANG , Kun YAO , Junyu Han , Errui DING , Jingdong WANG , Tian WU , Haifeng WANG
IPC: G06V30/19
CPC classification number: G06V30/19147 , G06V30/19167
Abstract: The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.
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35.发明公开公开(公告)号：US20230159052A1
公开(公告)日：2023-05-25
申请号：US18151718
申请日：2023-01-09
Inventor： Jianzhong YANG , Deguo XIA , Jizhou HUANG , Haifeng WANG
CPC classification number: B60W60/001 , B60W40/06 , B60W2520/00 , B60W2552/00 , B60W2556/10
Abstract: A method for processing behavior data, a method for controlling an autonomous vehicle, apparatuses thereof, a device, a storage medium, a computer program product, and an autonomous vehicle are provided. The method includes: acquiring historical driving data, the historical driving data comprising lane-level navigation data; and performing data mining on the historical driving data to obtain driving feature information, the driving feature information comprising at least one of: a lane-change position feature, a traveling speed feature, or a traveling path feature.
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Patent Agency Ranking
36.发明申请公开(公告)号：US20230092736A1
公开(公告)日：2023-03-23
申请号：US17872318
申请日：2022-07-25
Inventor： Wenbin JIANG , Yajuan LYU , Yong ZHU , Hua WU , Haifeng WANG
Abstract: The present disclosure provides a method for processing intelligent question-answering, an intelligent question-answering system, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies, or the like. An implementation includes: acquiring an input question and input data information; and based on the question, the data information and a plurality of knowledge bases, deciding an answer to the question by multilayer appreciation using a plurality of understanding module layers.
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公开(公告)号：US20230086145A1
公开(公告)日：2023-03-23
申请号：US17936761
申请日：2022-09-29
Inventor： Wenbin JIANG , Yajuan LV , Yong ZHU , Hua WU , Haifeng WANG
IPC: G06F16/738 , G06N5/02
Abstract: A method of processing data, a device, and a medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, natural language technology, speech technology, deep learning and knowledge graph. The method of processing data includes: generating a video feature, a question feature and an answer feature based on acquired video data, acquired question data and acquired candidate answer data; determining a link relationship between the video feature, the question feature and the answer feature; and determining a matching result for the video data, the question data and the candidate answer data based on the link relationship.
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公开(公告)号：US20230018489A1
公开(公告)日：2023-01-19
申请号：US17862519
申请日：2022-07-12
Inventor： Wenbin JIANG , Yajuan LYU , Yong ZHU , Hua WU , Haifeng WANG
IPC: G06F16/242 , G06F16/21 , G06F16/245 , G06N5/02
Abstract: The present disclosure discloses a method for acquiring a structured question-answering (QA) model, a QA method and corresponding apparatuses, and relates to knowledge graph and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring training samples corresponding to N structured QA database types, the training samples including question samples, information of the structured QA database types and query instruction samples used by the question samples to query structured QA databases of the types, N being an integer greater than 1; and training a text generation model by using the training samples to obtain the structured QA model, wherein the question samples and the information of the structured QA database types are taken as input to the text generation model, and the query instruction samples are taken as target output of the text generation model.
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公开(公告)号：US20220293092A1
公开(公告)日：2022-09-15
申请号：US17828773
申请日：2022-05-31
Inventor： Siyu DING , Chao PANG , Shuohuan WANG , Yanbin ZHAO , Junyuan SHANG , Yu SUN , Shikun FENG , Hao TIAN , Hua WU , Haifeng WANG
Abstract: The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.
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公开(公告)号：US20210209471A1
公开(公告)日：2021-07-08
申请号：US17211146
申请日：2021-03-24
Inventor： Haifeng WANG , Xiaoguang HU , Dianhai YU
Abstract: The present application discloses a processor video memory optimization method and apparatus for deep learning training tasks, and relates to the technical field of artificial intelligence. In the method, by determining an optimal path for transferring a computing result, the computing result of a first computing unit is transferred to a second computing unit by using the optimal path. Thus, occupying the video memory is avoided, and meanwhile, a problem of low utilization rate of the computing unit of a GPU caused by video memory swaps is avoided, so that training speed of most tasks is hardly reduced.
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