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    CONSENSUS GRAPH LEARNING-BASED MULTI-VIEW CLUSTERING METHOD
    21.
    发明公开
    CONSENSUS GRAPH LEARNING-BASED MULTI-VIEW CLUSTERING METHOD 审中-公开

    公开(公告)号：US20240143699A1

    公开(公告)日：2024-05-02

    申请号：US18276047

    申请日：2021-12-07

    申请人： ZHEJIANG NORMAL UNIVERSITY

    发明人： Xinzhong ZHU ,  Huiying XU ,  Zhenglai LI ,  Chang TANG ,  Jianmin ZHAO

    IPC分类号： G06F18/2323 ,  G06F17/14

    CPC分类号： G06F18/2323 ,  G06F17/142

    摘要： A consensus graph learning-based multi-view clustering method includes: S11, inputting an original data matrix to obtain a spectral embedding matrix; S12, calculating a similarity graph matrix and a Laplacian matrix based on the spectral embedding matrix; S13, applying spectral clustering to the calculated similarity graph matrix to obtain spectral embedding representations; S14, stacking inner products of the normalized spectral embedding representations into a third-order tensor and using low-rank tensor representation learning to obtain a consistent distance matrix; S15, integrating spectral embedding representation learning and low-rank tensor representation learning into a unified learning framework to obtain a objective function; S16, solving the obtained objective function through an alternative iterative optimization strategy; S17, constructing a consistent similarity graph based on the solved result; and S18, applying spectral clustering to the consistent similarity graph to obtain a clustering result. A consistent similarity graph for clustering is constructed based on spectral embedding features.

    Resident area prediction method, apparatus, device, and storage medium
    24.
    发明授权
    Resident area prediction method, apparatus, device, and storage medium 有权

    公开(公告)号：US11829447B2

    公开(公告)日：2023-11-28

    申请号：US17173142

    申请日：2021-02-10

    申请人： BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

    发明人： Xinjiang Lu ,  Nengjun Zhu ,  Hui Xiong

    IPC分类号： G06F17/18 ,  G06F18/2323 ,  G06N3/02 ,  G06F18/25

    CPC分类号： G06F18/2323 ,  G06F17/18 ,  G06F18/25 ,  G06N3/02

    摘要： This disclosure discloses a resident area prediction method, apparatus, device and storage medium, involving artificial intelligence technology, big data, deep learning and multi-task learning. The specific implementation plan is: acquiring a resident area data of a target user, and the resident area data including the resident area of the target user and the corresponding resident time; obtaining an association relationship between the resident areas of the target user by inputting the resident area data into an area relationship model, and the area relationship model is used to reflect a position relationship between the areas; determining a time-sequence relationship between the areas visited by the target user, according to the association relationship, the resident time and the visiting POI data; predicting a target resident area of the target user, according to the time-sequence relationship and the basic attribute information of the target user.

    Systems and methods for unsupervised paraphrase mining
    27.
    发明授权
    Systems and methods for unsupervised paraphrase mining 有权

    公开(公告)号：US11741312B2

    公开(公告)日：2023-08-29

    申请号：US17008563

    申请日：2020-08-31

    申请人： Recruit Co., Ltd.

    发明人： Behzad Golshan ,  Chen Chen ,  Wang-Chiew Tan ,  Danni Ma

    IPC分类号： G06F40/35 ,  G06F40/268 ,  G06F40/284 ,  G06F18/2323

    CPC分类号： G06F40/35 ,  G06F18/2323 ,  G06F40/268 ,  G06F40/284

    摘要： Disclosed embodiments relate to aligning pairs of sentences. Techniques can include receiving a plurality of sentences; generating a graph for each of at least two sentences of the plurality of sentences, wherein generating a graph for each sentence of the at least two sentences comprises: identifying one or more tokens for the sentence; and connecting via edges the one or more tokens; generating a combined graph for the at least two sentences wherein generating a combined graph comprises: aligning the identified tokens of the at least two sentences of the plurality of sentences; identifying matching and non-matching tokens between the at least two sentences based on the alignment; and merging matching tokens into a combined graph node.

    AGNOSTIC GRAPH REPRESENTATION AND ENCODING IN HUMAN-READABLE FORM
    29.
    发明公开
    AGNOSTIC GRAPH REPRESENTATION AND ENCODING IN HUMAN-READABLE FORM 审中-公开

    公开(公告)号：US20240329923A1

    公开(公告)日：2024-10-03

    申请号：US18192894

    申请日：2023-03-30

    申请人： International Business Machines Corporation

    发明人： Andrew C. M. HICKS ,  Stephanie Carys SHUM ,  Anthony Gennaro MANGIACAPRA ,  Marcos ARAQUE FIALLOS ,  Daniel Nicolas GISOLFI

    IPC分类号： G06F5/01 ,  G06F18/2323

    CPC分类号： G06F5/01 ,  G06F18/2323

    摘要： A method for encoding 2D numerical data comprises determining encoding parameters for a received set of 2D numerical data and generating a set of encoded data from the set of 2D numerical data according to the encoding parameters. The encoding parameters indicate a transitional relationship among a plurality of consecutive data points and a unitization interval for sampling the first set of 2D numerical data. When generating the set of encoded data, the encoding method sets a starting point, samples the set of 2D numerical data according to the unitization interval, and determines a string as a value of each data point of the set of the encoded data. The string indicates a position of a present encoded data point in the set of the encoded data, the transitional relationship, and a difference of magnitude between a present encoded data point and an immediately preceding one.