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    Systems and methods for an accelerated and enhanced tuning of a model based on prior model tuning data
    1.
    发明授权
    Systems and methods for an accelerated and enhanced tuning of a model based on prior model tuning data 有权

    公开(公告)号：US12159209B2

    公开(公告)日：2024-12-03

    申请号：US17071929

    申请日：2020-10-15

    Applicant: Intel Corporation

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

    IPC: G06N20/20 ,  G06F18/21 ,  G06F18/211 ,  G06F18/22 ,  G06F18/23

    Abstract: Systems and methods for an accelerated tuning of hyperparameters of a model supported with prior learnings data include assessing subject models associated with a plurality of distinct sources of transfer tuning data, wherein the assessing includes implementing of: [1] a model relatedness assessment for each of a plurality of distinct pairwise subject models, and [2] a model coherence assessment for each of the plurality of distinct pairwise subject models; constructing a plurality of distinct prior mixture models based on the relatedness metric value and the coherence metric value for each of the plurality of distinct pairwise subject models, identifying sources of transfer tuning data based on identifying a distinct prior mixture model having a satisfactory model evidence fraction; and accelerating a tuning of hyperparameters of the target model based on transfer tuning data associated with the distinct prior mixture model having the satisfactory model evidence fraction.

    Systems and methods for implementing an intelligent application program interface for an intelligent optimization platform
    3.
    发明授权
    Systems and methods for implementing an intelligent application program interface for an intelligent optimization platform 有权

    公开(公告)号：US11709719B2

    公开(公告)日：2023-07-25

    申请号：US17516296

    申请日：2021-11-01

    Applicant: Intel Corporation

    Inventor： Alexandra Johnson ,  Patrick Hayes ,  Scott Clark

    IPC: G06F9/54 ,  G06F11/34 ,  G06N20/00

    CPC classification number: G06F9/54 ,  G06F11/3495 ,  G06N20/00

    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the proposed hyperparameter values.

    Systems and methods for an accelerated tuning of hyperparameters of a model using a machine learning-based tuning service
    4.
    发明授权
    Systems and methods for an accelerated tuning of hyperparameters of a model using a machine learning-based tuning service 有权

    公开(公告)号：US11704567B2

    公开(公告)日：2023-07-18

    申请号：US16511320

    申请日：2019-07-15

    Applicant: Intel Corporation

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

    IPC: G06N3/082 ,  G06N20/00 ,  G06N20/20

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

    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 FOR AN ACCELERATED TUNING OF HYPERPARAMETERS OF A MODEL USING A MACHINE LEARNING-BASED TUNING SERVICE
    7.
    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
    8.
    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 implementing an intelligent application program interface for an intelligent optimization platform
    9.
    发明授权
    Systems and methods for implementing an intelligent application program interface for an intelligent optimization platform 有权

    公开(公告)号：US11163615B2

    公开(公告)日：2021-11-02

    申请号：US16741895

    申请日：2020-01-14

    Applicant: Intel Corporation

    Inventor： Alexandra Johnson ,  Patrick Hayes ,  Scott Clark

    IPC: G06F9/54 ,  G06F11/34 ,  G06N20/00

    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the proposed hyperparameter values.

    Systems and methods for tuning hyperparameters of a model and advanced curtailment of a training of the model
    10.
    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.

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