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1.发明申请公开(公告)号：US20230024680A1
公开(公告)日：2023-01-26
申请号：US17957275
申请日：2022-09-30
Inventor： Xinjiang LU , Dejing DOU
Abstract: A method of determining a regional land usage property, an electronic device and a storage medium, which relate to a field of an information technology, in particular to a field of a deep learning. The method includes: acquiring a human interaction information between a plurality of regions at a specified time; updating an initial representation vector of each of the regions according to the human interaction information, so as to obtain an embedding representation vector of each of the regions; selecting a target region from the regions, and selecting a plurality of static neighbor regions within a preset range around the target region; generating a feature map of the target region according to the embedding representation vector of the target region and the embedding representation vectors of the plurality of static neighbor regions; and predicting a land usage property of the target region by using the feature map.
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公开(公告)号：US20220292145A1
公开(公告)日：2022-09-15
申请号：US17829113
申请日：2022-05-31
Inventor： Yaqing WANG , Dejing DOU
IPC: G06F16/955 , G06F16/9538 , G06F16/9532 , G06F16/903 , G06F16/906
Abstract: A method for data processing is provided. The method includes obtaining first retrieving data associated with a first user and a first retrieving result selected by the first user from at least one retrieving result corresponding to the first retrieving data. The first retrieving data is labelled with an intention tag indicating a retrieving intention of the first user. The method further includes obtaining second retrieving data that is used by a second user to conduct retrieving and selecting the first retrieving result within a predetermined time period. The method further includes assigning the intention tag to the second retrieving data.
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公开(公告)号：US20220255724A1
公开(公告)日：2022-08-11
申请号：US17730988
申请日：2022-04-27
Inventor： Ji LIU , Qilong LI , Dejing DOU , Chongsheng ZHANG
IPC: H04L9/06 , G06V10/774 , G06V10/10
Abstract: The present disclosure provides a method and apparatus for determining an encryption mask, a method and apparatus for recognizing an image, a method and apparatus for training a model, a device, a storage medium and a computer program product. A specific implementation comprises: acquiring a test image set and an encryption mask set; superimposing an image in the test image set with a mask in the encryption mask set to obtain an encrypted image set; recognizing an image in the encrypted image set using a pre-trained encrypted image recognition model and recognizing the image in the encrypted image set using a pre-trained original image recognition model to obtain a first recognition result; and determining a target encryption mask from the encryption mask set based on the first recognition result.
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公开(公告)号：US20220059074A1
公开(公告)日：2022-02-24
申请号：US17520799
申请日：2021-11-08
Inventor： Yanyan LI , Dejing DOU
Abstract: A method for evaluating satisfaction with voice interaction, a device, and a storage medium are provided, which are related to a technical field of artificial intelligence, in particular, to fields of natural language processing, knowledge graph and deep learning, and can be applied to user intention understanding. The specific implementation includes: acquiring sample interaction data of a plurality of rounds of sample voice interaction behaviors; performing feature extractions on respective sample interaction data, to obtain a sample interaction feature sequence; acquiring satisfaction marks corresponding to the respective sample interaction data, to obtain a satisfaction mark sequence; and training an initial model by using a plurality of sets of sample interaction feature sequences and of satisfaction mark sequences, to obtain the model for evaluating satisfaction.
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5.发明公开公开(公告)号：US20240037410A1
公开(公告)日：2024-02-01
申请号：US18108977
申请日：2023-02-13
Inventor： Ji LIU , Beichen MA , Dejing DOU
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: A method for model aggregation in federated learning (FL), a server, a device, and a storage medium are suggested, which relate to the field of artificial intelligence (AI) technologies such as machine learning. A specific implementation solution involves: acquiring a data not identically and independently distributed (Non-IID) degree value of each of a plurality of edge devices participating in FL; acquiring local models uploaded by the edge devices; and performing aggregation based on the data Non-IID degree values of the edge devices and the local models uploaded by the edge devices to obtain a global model.
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Patent Agency Ranking
公开(公告)号：US20220398834A1
公开(公告)日：2022-12-15
申请号：US17820321
申请日：2022-08-17
Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD. , State Key Laboratory of Internet of Things for Smart City (University of Macau)
Inventor： Xingjian LI , Hang HUA , Chengzhong XU , Dejing DOU
IPC: G06V10/774
Abstract: A method for transfer learning includes: obtaining a pre-trained model, and generating a model to be transferred based on the pre-trained model, in which the model to be transferred includes N Transformer layers, and N is a positive integer; obtaining a mini-batch by performing random sampling on a target training set; and training the model to be transferred based on the mini-batch, in which a loss value for each Transformer layer is generated based on an empirical loss value and a noise stability loss value.
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公开(公告)号：US20220383198A1
公开(公告)日：2022-12-01
申请号：US17879888
申请日：2022-08-03
Inventor： Ji LIU , Chendi ZHOU , Shilei JI , Dejing DOU
Abstract: The present disclosure provides a method for asynchronous federated learning, including: in response to a request for participating in asynchronous federated learning sent by a target electronic device, determining, according to performance information of a server, a first number of electronic devices that the server supports to participate in the asynchronous federated learning, and acquiring a second number of other electronic devices that have participated in the asynchronous federated learning; if the first number is greater than the second number, sending a global model to be optimized to the target electronic device, and receiving target feedback information which is obtained by the target electronic device from training on the global model to be optimized; and optimizing, according to the target feedback information, the global model to be optimized to obtain an optimized global model.
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公开(公告)号：US20220028137A1
公开(公告)日：2022-01-27
申请号：US17450650
申请日：2021-10-12
Inventor： Yanyan LI , Airong JIANG , Dejing DOU
Abstract: A method for generating an electronic report, an electronic device and a storage medium, related to the field of large data and the field of artificial intelligence, are disclosed. The method for generating an electronic report includes: establishing a template tree comprising a plurality of branches, wherein the branches comprise at least one intermediate node and bottom layer nodes comprising identification information; and calling, for respective branches, data groups corresponding to the identification information of the bottom layer nodes from a database, respectively, and displaying the called data groups at positions corresponding to the bottom layer nodes in an electronic report. Labor consumption may be reduced, and advantages of low cost, high efficiency, automation and routinization may be achieved.
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公开(公告)号：US20230084055A1
公开(公告)日：2023-03-16
申请号：US17991958
申请日：2022-11-22
Inventor： Ji LIU , Sunjie YU , Dejing DOU , Jiwen ZHOU
Abstract: A method for generating a federated learning model is provided. The method includes obtaining images; obtaining sorting results of the images; and generating a trained federated learning model by training a federated learning model to be trained according to the images and the sorting results. The federated learning model to be trained is obtained after pruning a federated learning model to be pruned, and a pruning rate of a convolution layer in the federated learning model to be pruned is automatically adjusted according to a model accuracy during the pruning.
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公开(公告)号：US20230083116A1
公开(公告)日：2023-03-16
申请号：US17988264
申请日：2022-11-16
Inventor： Ji LIU , Hong ZHANG , Juncheng JIA , Jiwen ZHOU , Shengbo PENG , Ruipu ZHOU , Dejing DOU
Abstract: A federated learning method and system, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of computer vision and deep learning technologies. The method includes: performing a plurality of rounds of training until a training end condition is met, to obtain a trained global model; and publishing the trained global model to a plurality of devices. Each of the plurality of rounds of training includes: transmitting a current global model to at least some devices in the plurality of devices; receiving trained parameters for the current global model from the at least some devices; performing an aggregation on the received parameters to obtain a current aggregation model; and adjusting the current aggregation model based on a globally shared dataset, and updating the adjusted aggregation model as a new current global model for a next round of training.
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