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    Nonlinear optimization system
    2.
    发明授权
    Nonlinear optimization system 有权

    公开(公告)号：US11062219B1

    公开(公告)日：2021-07-13

    申请号：US17106488

    申请日：2020-11-30

    Applicant: SAS Institute Inc.

    Inventor： Joshua David Griffin ,  Riadh Omheni ,  Yan Xu

    IPC: G06N5/04 ,  G06N20/00

    Abstract: A computer solves a nonlinear optimization problem. An optimality check is performed for a current solution to an objective function that is a nonlinear equation with constraint functions on decision variables. When the performed optimality check indicates that the current solution is not an optimal solution, a barrier parameter value is updated, and a Lagrange multiplier value is updated for each constraint function based on a result of a complementarity slackness test. The current solution to the objective function is updated using a search direction vector determined by solving a primal-dual linear system that includes a dual variable for each constraint function and a step length value determined for each decision variable and for each dual variable. The operations are repeated until the optimality check indicates that the current solution is the optimal solution or a predefined number of iterations has been performed.

    Deep learning model training system
    3.
    发明授权
    Deep learning model training system 有权

    公开(公告)号：US10949747B1

    公开(公告)日：2021-03-16

    申请号：US16950145

    申请日：2020-11-17

    Applicant: SAS Institute Inc.

    Inventor： Majid Jahani ,  Joshua David Griffin ,  Seyedalireza Yektamaram ,  Wenwen Zhou

    IPC: G06N5/04 ,  G06N3/08 ,  G06F17/16 ,  G06K9/62 ,  G06N20/10

    Abstract: A computer trains a neural network model. (A) Observation vectors are randomly selected from a plurality of observation vectors. (B) A forward and backward propagation of a neural network is executed to compute a gradient vector and a weight vector. (C) A search direction vector is computed. (D) A step size value is computed. (E) An updated weight vector is computed. (F) Based on a predefined progress check frequency value, second observation vectors are randomly selected, a progress check objective function value is computed given the weight vector, the step size value, the search direction vector, and the second observation vectors, and based on an accuracy test, the mini-batch size value is updated. (G) (A) to (F) are repeated until a convergence parameter value indicates training of the neural network is complete. The weight vector for a next iteration is the computed updated weight vector.

    MULTI-OBJECTIVE DISTRIBUTED HYPERPARAMETER TUNING SYSTEM
    4.
    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.

    AUTOMATED MACHINE LEARNING TEST SYSTEM
    8.
    发明申请
    AUTOMATED MACHINE LEARNING TEST SYSTEM 有权

    公开(公告)号：US20220198340A1

    公开(公告)日：2022-06-23

    申请号：US17523607

    申请日：2021-11-10

    Applicant: SAS Institute Inc.

    Inventor： Yan Gao ,  Joshua David Griffin ,  Yu-Min Lin ,  Bengt Wisen Pederson ,  Ricky Dee Tharrington,, JR. ,  Pei-Yi Tan ,  Raymond Eugene Wright

    IPC: G06N20/20 ,  G06N7/00

    Abstract: A computing device selects new test configurations for testing software. Software under test is executed with first test configurations to generate a test result for each test configuration. Each test configuration includes a value for each test parameter where each test parameter is an input to the software under test. A predictive model is trained using each test configuration of the first test configurations in association with the test result generated for each test configuration based on an objective function value. The predictive model is executed with second test configurations to predict the test result for each test configuration of the second test configurations. Test configurations are selected from the second test configurations based on the predicted test results to define third test configurations. The software under test is executed with the defined third test configurations to generate the test result for each test configuration of the third test configurations.

    Distributed decision variable tuning system for machine learning
    9.
    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).

    Deep learning model training system
    10.
    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.

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