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公开(公告)号：US11514368B2
公开(公告)日：2022-11-29
申请号：US17246201
申请日：2021-04-30
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Jun Zhou
IPC: G06N20/00
Abstract: Current streaming sample data is received. A current deep learning model is trained based on the current streaming sample data, the training including: obtaining a shallow learning model through training based on historical sample data associated with the current streaming sample data; and initializing parameters of the current deep learning model as the parameters of the shallowing learning model.
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公开(公告)号：US11176418B2
公开(公告)日：2021-11-16
申请号：US16886609
申请日：2020-05-28
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Jun Zhou
Abstract: A sample is obtained from a test sample set. The sample is input into a plurality of models included in a model set that are to be tested, where the plurality of models include at least one neural network model. A plurality of output results are obtained, including obtaining, from each model of the plurality of models, a respective output result. A test result is determined based on the plurality of output results, where the test result includes at least one of a first test result or a second test result, where the first test result includes a plurality of output result accuracies. In response to determining that the test result does not satisfy a predetermined condition, a new sample is generated based on the sample and a predetermined rule, and the new sample is added to the test sample set.
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公开(公告)号：US11106802B2
公开(公告)日：2021-08-31
申请号：US16053606
申请日：2018-08-02
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Peilin Zhao , Jun Zhou , Xiaolong Li , Longfei Li
Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.
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公开(公告)号：US20210256423A1
公开(公告)日：2021-08-19
申请号：US17246201
申请日：2021-04-30
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Jun Zhou
IPC: G06N20/00
Abstract: Current streaming sample data is received. A current deep learning model is trained based on the current streaming sample data, the training including: obtaining a shallow learning model through training based on historical sample data associated with the current streaming sample data; and initializing parameters of the current deep learning model as the parameters of the shallowing learning model.
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公开(公告)号：US10917425B2
公开(公告)日：2021-02-09
申请号：US16882084
申请日：2020-05-22
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Ziqi Liu , Chaochao Chen , Jun Zhou , Xiaolong Li
IPC: H04L29/06 , G06N20/00 , G06F16/901 , G06F21/55 , G06N5/04
Abstract: Implementations of the present specification disclose graph structure model training and junk account identification methods, apparatuses, and devices. The solution includes: obtaining an account medium network graph, a node in the account medium network graph representing an account and a medium, and at least some edges indicating that a login behavior relationship exists between nodes connected by the edges; obtaining feature data and risk labeling data of the node, the feature data reflecting a login behavior of the corresponding node in a time sequence; and training, based on the account medium network graph, the feature data, and the risk labeling data, a predefined graph structure model for identifying a junk account.
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Patent Agency Ranking
公开(公告)号：US11551110B2
公开(公告)日：2023-01-10
申请号：US16290208
申请日：2019-03-01
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Chaochao Chen , Jun Zhou
Abstract: A client device determines a local user gradient value based on a current user preference vector and a local item gradient value based on a current item feature vector. The client device updates a user preference vector by using the local user gradient value and updates an item feature vector by using the local item gradient value. The client device determines a neighboring client device based on a predetermined adjacency relationship. The local item gradient value is sent by the client device to the neighboring client device. The client device receives a neighboring item gradient value sent by the neighboring client device. The client device updates the item feature vector by using the neighboring item gradient value. In response to the client device determining that a predetermined iteration stop condition is satisfied, the client device outputs the user preference vector and the item feature vector.
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公开(公告)号：US11269966B2
公开(公告)日：2022-03-08
申请号：US16918869
申请日：2020-07-01
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Xinxing Yang , Jun Zhou , Xiaolong Li
IPC: G06F16/00 , G06F16/9535 , G06K9/62
Abstract: The present specification discloses a multi-classifier-based recommendation method and device, and an electronic device. In the method, a target sub-classifier is obtained from k sub-classifiers in a multi-classifier based on received data, where the target sub-classifier is a sub-classifier whose data distribution has a highest similarity with feature data in the multi-classifier; the first prediction data obtained after the primary classifier performs prediction on the feature data is obtained, the second prediction data obtained after the target sub-classifier performs prediction on the feature data is obtained, and then the third prediction data is obtained by combining the first prediction data and the second prediction data, so that more accurate prediction data is obtained, thereby resolving the technical problem of low accuracy of data classification prediction in the existing technology; and the feature data is recommended based on the third prediction data, so that the accuracy of data recommendation is improved.
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公开(公告)号：US20210248499A1
公开(公告)日：2021-08-12
申请号：US17244811
申请日：2021-04-29
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Chaochao Chen , Liang Li , Jun Zhou
Abstract: A first training participant performs an iterative process until a predetermined condition is satisfied, where the iterative process includes: obtaining, using secret sharing matrix addition and based on the current sub-model of each training participant and a corresponding feature sample subset of each training participant, a current prediction value of the regression model for a feature sample set, where the corresponding feature sample subset of each training participant is obtained by performing vertical segmentation on the feature sample set; determining a prediction difference between the current prediction value and a label corresponding to the current prediction value; sending the prediction difference to each second training participant; and updating a current sub-model of the first training participant based on the current sub-model of the first training participant and a product of a corresponding feature sample subset of the first training participant and the prediction difference.
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公开(公告)号：US11074246B2
公开(公告)日：2021-07-27
申请号：US16805079
申请日：2020-02-28
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Shaosheng Cao , Xinxing Yang , Jun Zhou
Abstract: Implementations of the present specification disclose method, apparatus, and device for processing graph data using a random walk-based process. The process is applicable to either a cluster of machines, a stand-alone machine, or both. In one aspect, the method includes: obtaining, by a cluster, data describing a graph that has nodes and edges between the nodes, wherein the cluster comprises (i) a server cluster that includes a plurality of server machines and (ii) a working machine cluster that includes a plurality of working machines; generating a two-dimensional array based on the data, wherein generating the two-dimensional array comprises generating, for each node included in the graph, a row comprising respective identifiers of adjacent nodes of the node; and generating, based on the two-dimensional array, a random sequence that represents a random walk processing of the data by the cluster.
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公开(公告)号：US10891161B2
公开(公告)日：2021-01-12
申请号：US16907637
申请日：2020-06-22
Applicant: Advanced New Technologies Co., Ltd.
Inventor： Jun Zhou , Xiaolong Li
Abstract: Evaluation results of a plurality of users are received from a plurality of data providers. The evaluation results are obtained by the plurality of data providers evaluating the plurality of users based on evaluation models of the plurality of data providers. A plurality of training samples is constructed by using the evaluation results. Each training sample includes a respective subset of the evaluation results corresponding to a same user of the plurality of users. A label for each training sample is generated based on an actual service execution status of the same user. A model is trained based on the plurality of training samples and the plurality of labels, including setting a plurality of variable coefficients, each variable coefficient specifying a contribution level of a corresponding data provider. Virtual resources to each data provider are allocated based on the plurality of variable coefficients.
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