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    DISTRIBUTABLE EVENT PREDICTION AND MACHINE LEARNING RECOGNITION SYSTEM
    1.
    发明申请
    DISTRIBUTABLE EVENT PREDICTION AND MACHINE LEARNING RECOGNITION SYSTEM 有权

    公开(公告)号：US20210287116A1

    公开(公告)日：2021-09-16

    申请号：US17178798

    申请日：2021-02-18

    Applicant: SAS Institute Inc

    Inventor： Xu Chen ,  Jorge Manuel Gomes da Silva ,  Brett Alan Wujek

    IPC: G06N5/04 ,  G06N20/00

    Abstract: Data is classified using semi-supervised data. Sparse coefficients are computed using a decomposition of a Laplacian matrix. (B) Updated parameter values are computed for a dimensionality reduction method using the sparse coefficients, the Laplacian matrix, and a plurality of observation vectors. The updated parameter values include a robust estimator of a decomposition matrix determined from the decomposition of the Laplacian matrix. (B) is repeated until a convergence parameter value indicates the updated parameter values for the dimensionality reduction method have converged. A classification matrix is defined using the sparse coefficients and the robust estimator of the decomposition of the Laplacian matrix. The target variable value is determined for each observation vector based on the classification matrix. The target variable value is output for each observation vector of the plurality of unclassified observation vectors and is defined to represent a label for a respective unclassified observation vector.

    Analytic system based on multiple task learning with incomplete data
    2.
    发明授权
    Analytic system based on multiple task learning with incomplete data 有权

    公开(公告)号：US10474959B2

    公开(公告)日：2019-11-12

    申请号：US16445593

    申请日：2019-06-19

    Applicant: SAS Institute Inc.

    Inventor： Xin Jiang Hunt ,  Saba Emrani ,  Jorge Manuel Gomes da Silva ,  Ilknur Kaynar Kabul

    IPC: G06N7/00 ,  G06N20/00 ,  G06F17/18 ,  G06F17/16 ,  G16H50/70 ,  G06N5/00 ,  G16H50/20

    Abstract: A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

    ANALYTIC SYSTEM BASED ON MULTIPLE TASK LEARNING WITH INCOMPLETE DATA
    3.
    发明申请
    ANALYTIC SYSTEM BASED ON MULTIPLE TASK LEARNING WITH INCOMPLETE DATA 审中-公开

    公开(公告)号：US20180336484A1

    公开(公告)日：2018-11-22

    申请号：US15833641

    申请日：2017-12-06

    Applicant: SAS Institute Inc.

    Inventor： Xin Jiang Hunt ,  Saba Emrani ,  Jorge Manuel Gomes da Silva ,  Ilknur Kaynar Kabul

    IPC: G06N7/00 ,  G06F17/18 ,  G06F17/16 ,  G06N99/00

    CPC classification number: G06N7/005 ,  G06F17/16 ,  G06F17/18 ,  G06N5/003 ,  G06N20/00 ,  G16H50/20 ,  G16H50/70

    Abstract: A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

    MACHINE LEARNING PREDICTIVE LABELING SYSTEM
    7.
    发明申请
    MACHINE LEARNING PREDICTIVE LABELING SYSTEM 审中-公开

    公开(公告)号：US20190325344A1

    公开(公告)日：2019-10-24

    申请号：US16404789

    申请日：2019-05-07

    Applicant: SAS Institute Inc.

    Inventor： Xu CHEN ,  Jorge Manuel Gomes da Silva

    IPC: G06N20/00 ,  G06N7/00 ,  G06F17/16

    Abstract: A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix used to select a label for each observation. Unique combinations of observations selected from the unlabeled observations are defined. A marginal distribution value is computed from the label distribution probability matrix. A joint distribution value is computed between observations included in each combination. A mutual information value is computed for each combination as a combination of the marginal distribution value and the joint distribution value computed for the respective combination. A predefined number of observation vector combinations is selected from the combinations that have highest values for the computed mutual information value. Labeled observation vectors are updated to include each observation vector included in the selected observation vector combinations with a respective obtained label.

    Analytic system based on multiple task learning with incomplete data
    8.
    发明授权
    Analytic system based on multiple task learning with incomplete data 有权

    公开(公告)号：US10402741B2

    公开(公告)日：2019-09-03

    申请号：US15833641

    申请日：2017-12-06

    Applicant: SAS Institute Inc.

    Inventor： Xin Jiang Hunt ,  Saba Emrani ,  Jorge Manuel Gomes da Silva ,  Ilknur Kaynar Kabul

    IPC: G06N7/00 ,  G06F17/16 ,  G06F17/18 ,  G06N20/00 ,  G16H10/00

    Abstract: A computing device computes a weight matrix to predict a value for a characteristic in a scoring dataset. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

    Monitoring, detection, and surveillance system using principal component analysis with machine and sensor data
    9.
    发明授权
    Monitoring, detection, and surveillance system using principal component analysis with machine and sensor data 有权

    公开(公告)号：US10157319B2

    公开(公告)日：2018-12-18

    申请号：US15894002

    申请日：2018-02-12

    Applicant: SAS Institute Inc.

    Inventor： Wei Xiao ,  Jorge Manuel Gomes da Silva ,  Saba Emrani ,  Arin Chaudhuri

    IPC: G06K9/62 ,  G06K9/00 ,  G06K9/48 ,  G06F17/16 ,  G06F17/18 ,  G06F9/30

    Abstract: A computing device detects an abnormal observation vector using a principal components decomposition. The principal components decomposition includes a sparse noise vector st computed for the observation vector that includes a plurality of values, wherein each value is associated with a variable to define a plurality of variables. The sparse noise vector st has a dimension equal to m a number of the plurality of variables. A zero counter time series value ĉt is computed using ĉt=Σi=1mst[i]. A probability value for ĉt is computed using p=Σi=ĉt+1m+1Hc[i]/Σi=0m+1Hc[i], where Hc[i] includes a count of a number of times each value of ĉt occurred for previous observation vectors. The probability value is compared with a predefined abnormal observation probability value. An abnormal observation indicator is set when the probability value indicates the observation vector is abnormal. The observation vector is output when the probability value indicates the observation vector is abnormal.

    Tabular data generation with attention for machine learning model training system
    10.
    发明授权
    Tabular data generation with attention for machine learning model training system 有权

    公开(公告)号：US11416712B1

    公开(公告)日：2022-08-16

    申请号：US17560474

    申请日：2021-12-23

    Applicant: SAS Institute Inc.

    Inventor： Amirhassan Fallah Dizche ,  Ye Liu ,  Xin Jiang Hunt ,  Jorge Manuel Gomes da Silva

    IPC: G06K9/62 ,  G06N3/04

    Abstract: A computing device generates synthetic tabular data. Until a convergence parameter value indicates that training of an attention generator model is complete, conditional vectors are defined; latent vectors are generated using a predefined noise distribution function; a forward propagation of an attention generator model that includes an attention model integrated with a conditional generator model is executed to generate output vectors; transformed observation vectors are selected; a forward propagation of a discriminator model is executed with the transformed observation vectors, the conditional vectors, and the output vectors to predict whether each transformed observation vector and each output vector is real or fake; a discriminator model loss value is computed based on the predictions; the discriminator model is updated using the discriminator model loss value; an attention generator model loss value is computed based on the predictions; and the attention generator model is updated using the attention generator model loss value.

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