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    SYSTEMS AND METHODS FOR IMPLEMENTING AN INTELLIGENT MACHINE LEARNING OPTIMIZATION PLATFORM FOR MULTIPLE TUNING CRITERIA
    1.
    发明公开
    SYSTEMS AND METHODS FOR IMPLEMENTING AN INTELLIGENT MACHINE LEARNING OPTIMIZATION PLATFORM FOR MULTIPLE TUNING CRITERIA 审中-公开

    公开(公告)号：US20230325721A1

    公开(公告)日：2023-10-12

    申请号：US18320803

    申请日：2023-05-19

    Applicant: Intel Corporation

    Inventor： Bolong Cheng ,  Olivia Kim ,  Michael McCourt ,  Patrick Hayes ,  Scott Clark

    IPC: G06N20/00 ,  G06F17/10

    CPC classification number: G06N20/00 ,  G06F17/10

    Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning func-tion based on a combination of the first objective function and the second objective function; executing a tuning opera-tion of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparam-eter-based points along a convex Pareto optimal curve.

    SYSTEMS AND METHODS FOR TUNING HYPERPARAMETERS OF A MODEL AND ADVANCED CURTAILMENT OF A TRAINING OF THE MODEL
    2.
    发明申请
    SYSTEMS AND METHODS FOR TUNING HYPERPARAMETERS OF A MODEL AND ADVANCED CURTAILMENT OF A TRAINING OF THE MODEL 有权

    公开(公告)号：US20220114450A1

    公开(公告)日：2022-04-14

    申请号：US17508665

    申请日：2021-10-22

    Applicant: Intel Corporation

    Inventor： Michael McCourt ,  Taylor Jackie Springs ,  Ben Hsu ,  Simon Howey ,  Halley Nicki Vance ,  James Blomo ,  Patrick Hayes ,  Scott Clark

    IPC: G06N3/08

    Abstract: A system and method for tuning hyperparameters and training a model includes implementing a hyperparameter tuning service that tunes hyperparameters of a model that includes receiving, via an API, a tuning request that includes: (i) a first part comprising tuning parameters for generating tuned hyperparameter values for hyperparameters of the model; and (ii) a second part comprising model training control parameters for monitoring and controlling a training of the model, wherein the model training control parameters include criteria for generating instructions for curtailing a training run of the model; monitoring the training run for training the model based on the second part of the tuning request, wherein the monitoring of the training run includes periodically collecting training run data; and computing an advanced training curtailment instruction based on the training run data that automatically curtails the training run prior to a predefined maximum training schedule of the training run.

    SYSTEMS AND METHODS FOR AN ACCELERATED TUNING OF HYPERPARAMETERS OF A MODEL USING A MACHINE LEARNING-BASED TUNING SERVICE
    3.
    发明公开
    SYSTEMS AND METHODS FOR AN ACCELERATED TUNING OF HYPERPARAMETERS OF A MODEL USING A MACHINE LEARNING-BASED TUNING SERVICE 审中-公开

    公开(公告)号：US20230325672A1

    公开(公告)日：2023-10-12

    申请号：US18320758

    申请日：2023-05-19

    Applicant: Intel Corporation

    Inventor： Michael McCourt ,  Ben Hsu ,  Patrick Hayes ,  Scott Clark

    IPC: G06N3/082 ,  G06N20/00

    CPC classification number: G06N3/082 ,  G06N20/20 ,  G06N20/00

    Abstract: A system and method for accelerated tuning of hyperparameters includes receiving a multi-task tuning work request for tuning hyperparameters of a model, wherein the multi-task tuning work request includes: a full tuning task for tuning hyperparameters, wherein the full tuning task includes a first set of tuning parameters governing a first tuning operation; a partial tuning task for tuning the hyperparameters of the model, wherein the partial tuning task includes a second distinct set of tuning parameters governing a second tuning operation; executing the first tuning operation and the second tuning operation; generating a first suggestion set and a second suggestion set of one or more proposed values for the hyperparameters based on the execution of the full tuning task and the partial tuning task; and setting the partial tuning task as a proxy for the full tuning task thereby accelerating a tuning of the hyperparameters of the model.

    Systems and methods implementing an intelligent optimization platform
    4.
    发明授权
    Systems and methods implementing an intelligent optimization platform 有权

    公开(公告)号：US11301781B2

    公开(公告)日：2022-04-12

    申请号：US16796489

    申请日：2020-02-20

    Applicant: Intel Corporation

    Inventor： Patrick Hayes ,  Michael McCourt ,  Alexandra Johnson ,  George Ke ,  Scott Clark

    IPC: G06N20/00 ,  G06F9/54 ,  G06N20/20 ,  G06N5/00 ,  G06N7/00 ,  G06N99/00

    Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

    Systems and methods for tuning hyperparameters of a model and advanced curtailment of a training of the model
    5.
    发明授权
    Systems and methods for tuning hyperparameters of a model and advanced curtailment of a training of the model 有权

    公开(公告)号：US11157812B2

    公开(公告)日：2021-10-26

    申请号：US16849422

    申请日：2020-04-15

    Applicant: Intel Corporation

    Inventor： Michael McCourt ,  Taylor Jackie Springs ,  Ben Hsu ,  Simon Howey ,  Halley Nicki Vance ,  James Blomo ,  Patrick Hayes ,  Scott Clark

    IPC: G06N3/08

    Abstract: A system and method for tuning hyperparameters and training a model includes implementing a hyperparameter tuning service that tunes hyperparameters of a model that includes receiving, via an API, a tuning request that includes: (i) a first part comprising tuning parameters for generating tuned hyperparameter values for hyperparameters of the model; and (ii) a second part comprising model training control parameters for monitoring and controlling a training of the model, wherein the model training control parameters include criteria for generating instructions for curtailing a training run of the model; monitoring the training run for training the model based on the second part of the tuning request, wherein the monitoring of the training run includes periodically collecting training run data; and computing an advanced training curtailment instruction based on the training run data that automatically curtails the training run prior to a predefined maximum training schedule of the training run.

    Systems and methods implementing an intelligent machine learning tuning system providing multiple tuned hyperparameter solutions
    7.
    发明授权
    Systems and methods implementing an intelligent machine learning tuning system providing multiple tuned hyperparameter solutions 有权

    公开(公告)号：US11270217B2

    公开(公告)日：2022-03-08

    申请号：US16194192

    申请日：2018-11-16

    Applicant: Intel Corporation

    Inventor： Kevin Tee ,  Michael McCourt ,  Patrick Hayes ,  Scott Clark

    IPC: G06N7/00 ,  G06N20/00

    Abstract: Systems and methods include receiving a tuning work request for tuning hyperparameters of a third-party model or system; performing, by a machine learning-based tuning service, a first tuning of the hyperparameters in a first tuning region; identifying tuned hyperparameter values for each of the hyperparameters based on results of the first tuning; setting a failure region based on the tuned hyperparameter values of the first tuning; performing, by the machine learning-based tuning service, a second tuning of the hyperparameters in a second tuning region that excludes the failure region; identifying additional tuned hyperparameter values for each of the hyperparameters based on results of the second tuning; and returning the tuned hyperparameter values and the additional hyperparameter values for implementing the third-party model or system with one of the tuned hyperparameter values and the additional hyperparameter values.

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