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1.发明申请公开(公告)号：US20230072240A1
公开(公告)日：2023-03-09
申请号：US17988168
申请日：2022-11-16
Inventor： Kafeng WANG , Chengzhong XU , Haoyi XIONG , Xingjian LI , Dejing DOU
IPC: G06K9/62
Abstract: A method for processing synthetic features is provided, and includes: the synthetic features to be evaluated and original features corresponding to the synthetic features are obtained. A feature extraction is performed on the synthetic features to be evaluated based on a number S of pre-trained samples, to obtain meta features with S samples. S is a positive integer. The meta features are input into the pre-trained meta feature evaluation model for a binary classification prediction, to obtain a probability of binary classification. Quality screening is performed on the synthetic features to be evaluated according to the probability of the binary classification, to obtain second synthetic features to be evaluated. The second synthetic features are classified in a good category. The second synthetic features and original features are input into a first classifier for evaluation. classified in a poor category.
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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：US20220398244A1
公开(公告)日：2022-12-15
申请号：US17890366
申请日：2022-08-18
Inventor： Yanyan LI , Haoyi XIONG , Jiang BIAN , Zheng GONG , Ruyue MA , Dejing DOU
IPC: G06F16/242 , G06F16/28
Abstract: A query method is provided and includes: acquiring association records, in which the association record is configured to indicate an execution area, execution time and user attribute data of an execution user, of a behavior; splitting the association record into behavior records based on attribute items included in the user attribute data of the association record, in which the behavior record is configured to indicate a mapping relationship between at least one of the attribute items and the execution area-the execution time; grouping the behavior records to determine behavior statistics information of each group; in which behavior records having the same attribute item, the same execution area and the same execution time belong to the same group; and displaying behavior statistics information of a target group in response to a query operation.
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公开(公告)号：US20220392199A1
公开(公告)日：2022-12-08
申请号：US17819777
申请日：2022-08-15
Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD. , State Key Laboratory of Internet of Things for Smart City (University of Macau)
Inventor： Kafeng WANG , Chengzhong XU , Haoyi XIONG , Xingjian LI , Dejing DOU
IPC: G06V10/774 , G06V10/764 , G06V10/82 , G06V10/778
Abstract: A method and an apparatus for training a classification model and data classification includes: obtaining a sample set and a pre-trained classification model, wherein the classification model includes at least two convolutional layers, each convolutional layer is connected to a classification layer through a fully connected layer; inputting the sample set into the classification model, and obtaining a prediction result output by each classification layer, wherein the prediction result includes a prediction probability of a class to which each sample belongs; calculating a probability threshold of each classification layer based on the prediction result output by each classification layer; setting a prediction stopping condition for the classification mode according to the probability threshold of each classification layer.
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5.发明申请公开(公告)号：US20220130495A1
公开(公告)日：2022-04-28
申请号：US17570505
申请日：2022-01-07
Inventor： Shuangli LI , Jingbo ZHOU , Liang HUANG , Haoyi XIONG , Fan WANG , Tong XU , Hui XIONG , Dejing DOU
Abstract: A method for determining correlation between a drug and a target, and an electronic device are provided. The method includes: establishing a spatial molecular graph of a candidate drug and the target, the spatial molecular graph including an atomic node set and an edge set, the atomic node set including atoms in the candidate drug and atoms in the target, the edge set including at least one atom connection edge; inputting a first atom feature of the atomic node set and the spatial molecular graph into a first GAT for prediction, to obtain a second atom feature of the atomic node set; and determining a parameter value of the correlation between the candidate drug and the target in accordance with the second atom feature of the atomic node set.
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公开(公告)号：US20210319262A1
公开(公告)日：2021-10-14
申请号：US17355347
申请日：2021-06-23
Inventor： Xingjian LI , Haoyi XIONG , Dejing DOU
Abstract: The present application provides a model training, image processing method, device, storage medium, and program product relating to deep learning technology, which are able to screen auxiliary image data with image data for learning a target task, and further fuse the target image data and the auxiliary image data, so as to train a built and to-be-trained model with the fusion-processed fused image data. This implementation can increase the amount of data for training the model, and the data for training the model is determined is based on the target image data, which is suitable for learning the target task. Therefore, the solution provided by the present application can train an accurate target model even if the amount of target image data is not sufficient.
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