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    Recommendation system
    2.
    发明授权
    Recommendation system 有权

    公开(公告)号：US11379743B2

    公开(公告)日：2022-07-05

    申请号：US17386853

    申请日：2021-07-28

    Applicant: SAS Institute Inc.

    Inventor： Xuejun Liao ,  Patrick Nathan Koch ,  Shunping Huang ,  Yan Xu

    IPC: G06N5/04 ,  G06Q30/06 ,  G06F17/16

    Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.

    Distributed decision variable tuning system for machine learning
    3.
    发明授权
    Distributed decision variable tuning system for machine learning 有权

    公开(公告)号：US10963802B1

    公开(公告)日：2021-03-30

    申请号：US17120340

    申请日：2020-12-14

    Applicant: SAS Institute Inc.

    Inventor： Steven Joseph Gardner ,  Joshua David Griffin ,  Yan Xu ,  Yan Gao

    IPC: G06N5/04 ,  G06N20/00

    Abstract: A computing device selects decision variable values. A lower boundary value and an upper boundary value is defined for a decision variable. (A) A plurality of decision variable configurations is determined using a search method. The value for the decision variable is between the lower boundary value and the upper boundary value. (B) A decision variable configuration is selected. (C) A model of the model type is trained using the decision variable configuration. (D) The model is scored to compute an objective function value. (E) The computed objective function value and the selected decision variable configuration are stored. (F) (B) through (E) is repeated for a plurality of decision variable configurations. (G) The lower boundary value and the upper boundary value are updated using the objective function value and the decision variable configuration stored. Repeat (A)-(F) with the lower boundary value and the upper boundary value updated in (G).

    Accelerating configuration of machine-learning models
    4.
    发明授权
    Accelerating configuration of machine-learning models 有权

    公开(公告)号：US10776721B1

    公开(公告)日：2020-09-15

    申请号：US16726710

    申请日：2019-12-24

    Applicant: SAS Institute Inc.

    Inventor： Rui Shi ,  Seyedalireza Yektamaram ,  Yan Xu

    IPC: G06N3/06 ,  G06N99/00 ,  G06N3/063 ,  A61B5/00 ,  G06N20/00 ,  G06F40/20 ,  G06N3/04

    Abstract: Machine-learning models (MLM) can be configured more rapidly using some examples described herein. For example, a MLM can be configured by executing an iterative process, where each iteration includes a series of operations. The series of operations can include determining a current weight value for the current iteration, determining a current gradient direction for the current iteration based on the current weight value, and determining a current learning rate for the current iteration based on the current gradient direction. The operations can also include determining a current multistage momentum value for the current iteration. A next weight value for a next iteration can then be determined based on (i) the current weight value, (ii) the current gradient direction, (iii) the current learning rate, and (iv) the current multistage momentum value. The next weight value may also be determined based on a predefined learning rate that was preset, in some examples.

    Deep learning model training system
    5.
    发明授权
    Deep learning model training system 有权

    公开(公告)号：US10769528B1

    公开(公告)日：2020-09-08

    申请号：US16590544

    申请日：2019-10-02

    Applicant: SAS Institute Inc.

    Inventor： Ben-hao Wang ,  Joshua David Griffin ,  Seyedalireza Yektamaram ,  Yan Xu

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A computer trains a neural network model. (B) A neural network is executed to compute a post-iteration gradient vector and a current iteration weight vector. (C) A search direction vector is computed using a Hessian approximation matrix and the post-iteration gradient vector. (D) A step size value is initialized. (E) An objective function value is computed that indicates an error measure of the executed neural network. (F) When the computed objective function value is greater than an upper bound value, the step size value is updated using a predefined backtracking factor value. The upper bound value is computed as a sliding average of a predefined upper bound updating interval value number of previous upper bound values. (G) (E) and (F) are repeated until the computed objective function value is not greater than the upper bound value. (H) An updated weight vector is computed to describe a trained neural network model.

    MULTI-OBJECTIVE DISTRIBUTED HYPERPARAMETER TUNING SYSTEM
    7.
    发明申请
    MULTI-OBJECTIVE DISTRIBUTED HYPERPARAMETER TUNING SYSTEM 有权

    公开(公告)号：US20210264287A1

    公开(公告)日：2021-08-26

    申请号：US17081118

    申请日：2020-10-27

    Applicant: SAS Institute Inc.

    Inventor： Steven Joseph Gardner ,  Joshua David Griffin ,  Yan Xu ,  Patrick Nathan Koch ,  Brett Alan Wujek ,  Oleg Borisovich Golovidov

    IPC: G06N3/12 ,  G06N20/00 ,  G06N5/00

    Abstract: Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration. The computed projection and the selected hyperparameter configuration are stored to the first cache, and the selected hyperparameter configuration is replaced with the computed projection.

    Cubic regularization optimizer
    9.
    发明授权
    Cubic regularization optimizer 有权

    公开(公告)号：US11983631B1

    公开(公告)日：2024-05-14

    申请号：US18511092

    申请日：2023-11-16

    Applicant: SAS Institute Inc.

    Inventor： Wenwen Zhou ,  Joshua David Griffin ,  Riadh Omheni ,  Seyedalireza Yektamaram ,  Yan Xu

    IPC: G06N3/08 ,  G06F17/16

    CPC classification number: G06N3/08 ,  G06F17/16

    Abstract: A computer determines a solution to a nonlinear optimization problem. A conjugate gradient (CG) iteration is performed with a first order derivative vector and a second order derivative matrix to update a CG residual vector, an H-conjugate vector, and a residual weight vector. A CG solution vector is updated using a previous CG solution vector, the H-conjugate vector, and the residual weight vector. An eigenvector of the second order derivative matrix having a smallest eigenvalue is computed. A basis matrix is defined that includes a cubic regularization (CR) solution vector, a CR residual vector, the CG solution vector, the CG residual vector, and the eigenvector. A CR iteration is performed to update the CR solution vector. The CR residual vector is updated using the first order derivative vector, the second order derivative matrix, and the updated CR solution vector. The process is repeated until a stop criterion is satisfied.

    RECOMMENDATION SYSTEM
    10.
    发明申请
    RECOMMENDATION SYSTEM 有权

    公开(公告)号：US20220138605A1

    公开(公告)日：2022-05-05

    申请号：US17386853

    申请日：2021-07-28

    Applicant: SAS Institute Inc.

    Inventor： Xuejun Liao ,  Patrick Nathan Koch ,  Shunping Huang ,  Yan Xu

    IPC: G06N5/04 ,  G06F17/16 ,  G06Q30/06

    Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.

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