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公开(公告)号：US20230074417A1
公开(公告)日：2023-03-09
申请号：US18055149
申请日：2022-11-14
Inventor： Ji LIU , Sunjie YU , Jiwen ZHOU , Ruipu ZHOU , Dejing DOU
Abstract: A method for training a longitudinal federated learning model is provided, and is applied to a first participant device. The first participant device includes label data. The longitudinal federated learning model includes a first bottom layer sub-model, an interaction layer sub-model, a top layer sub-model based on a Lipschitz neural network and a second bottom layer sub-model in a second participant device. First bottom layer output data of the first participant device and second bottom layer output data sent by the second participant device are obtained. The first bottom layer output data and the second bottom layer output data are input into an interaction layer sub-model to obtain interaction layer output data. Top layer output data is obtained based on the interaction layer output data and the top layer sub-model. The longitudinal federated learning model is trained according to the top layer output data and the label data.
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22.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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公开(公告)号：US20230004614A1
公开(公告)日：2023-01-05
申请号：US17729282
申请日：2022-04-26
Inventor： Yanyan LI , Airong JIANG , Shilin WU , Dejing DOU
IPC: G06F16/9538 , G06F16/9537 , G06F16/29 , G06N20/00
Abstract: The present disclosure discloses a method and apparatus for displaying map points of interest, and an electronic device, relates to the field of artificial intelligence, and in particular to intelligent transportation. A specific implementation solution includes: acquiring features corresponding to multiple candidate points of interest; determining predicted popularity of the multiple candidate points of interest according to a mapping relation between each feature and each popularity and the features of the multiple candidate points of interest, and the mapping relation is determined based on the frequency of operations performed by a user for each sample point of interest in a historical time period; and displaying the candidate points of interest of which predicted popularity meets a preset popularity condition in a map. Therefore, the accuracy of the displayed points of interest may be enhanced.
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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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Patent Agency Ranking
公开(公告)号：US20220237388A1
公开(公告)日：2022-07-28
申请号：US17714891
申请日：2022-04-06
Inventor： Xinjiang LU , Yanyan LI , Jingbo ZHOU , Dejing DOU
IPC: G06F40/40 , G06F40/30 , G06F40/177 , G06F40/20
Abstract: A method and apparatus for generating a table description text, a device, and a storage medium are provided. An implementation of the method includes: acquiring a to-be-described table, and analyzing the to-be-described table to obtain a set of metalanguage of the to-be-described table, and finally generating a description text of the to-be-described table based on the metalanguage in the set of metalanguage.
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27.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：US20220092418A1
公开(公告)日：2022-03-24
申请号：US17457649
申请日：2021-12-03
Inventor： Hao LIU , Jindong HAN , Dejing DOU
Abstract: Provided are a training method for an air quality prediction model, a prediction method and apparatus, a device, a program, and a medium. The method includes the steps described below. A target monitoring range is divided into a plurality of regions; the air quality prediction model is pre-trained by adopting a pre-training sample and a pre-training objective function, where the pre-training sample includes measurement values; and the pre-trained air quality prediction model is trained by adopting a formal training sample and a formal training objective function, where the formal training sample includes the measurement values. The air quality prediction model is configured to predict air quality of the plurality of regions according to spatial information, historical information and environmental information.
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公开(公告)号：US20220082397A1
公开(公告)日：2022-03-17
申请号：US17531529
申请日：2021-11-19
Inventor： Weijia ZHANG , Hao LIU , Dejing DOU , Hui XIONG
Abstract: A method for recommending a station for a vehicle, a device, and a storage medium are provided. The method comprises: receiving, by a server, an access request from a vehicle; obtaining, based on the access request, a plurality of observation values from a plurality of stations associated with the vehicle, respectively, each observation value is based on a corresponding pre-trained recommendation model, each observation value includes factors associated with access of the vehicle to the station corresponding to the observation value; determining, an action value for the station based on the observation value and the pre-trained recommendation model for the station, the action value for the station indicates a matching degree between the access request and the station; determining a recommended station among the plurality of stations based on the action values of the plurality of stations; and sending to the vehicle an instruction of driving to the recommended station.
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30.发明公开公开(公告)号：US20240086717A1
公开(公告)日：2024-03-14
申请号：US18098514
申请日：2023-01-18
Inventor： Ji LIU , Hao TIAN , Ruipu ZHOU , Dejing DOU
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: Disclosed is a model training control method based on asynchronous federated learning, an electronic device and a storage medium, relating to data processing technical field, and especially to technical fields such as edge computing and machine learning. The method includes: sending a first parameter of a first global model to a plurality of edge devices; receiving a second parameter of a second global model returned by a first edge device of plurality of edge devices, the second global model being a global model obtained after the first edge device trains the first global model according to a local data set; and sending a third parameter of a third global model to a second edge device of the plurality of edge devices in a case of the third global model is obtained based on aggregation of at least one second global model.
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