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11.发明授权公开(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.
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公开(公告)号：US12141667B2
公开(公告)日：2024-11-12
申请号：US17561480
申请日：2021-12-23
Applicant: Intel Corporation
Inventor： Patrick Hayes , Michael McCourt , Alexandra Johnson , George Ke , Scott Clark
Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel; running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.
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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
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.
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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
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.
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公开(公告)号：US11699098B2
公开(公告)日：2023-07-11
申请号：US16895099
申请日：2020-06-08
Applicant: Intel Corporation
Inventor： Bolong Cheng , Olivia Kim , Michael McCourt , Patrick Hayes , Scott Clark
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 function based on a combination of the first objective function and the second objective function; executing a tuning operation 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 hyperparameter-based points along a convex Pareto optimal curve.
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Patent Agency Ranking
公开(公告)号：US20220121993A1
公开(公告)日：2022-04-21
申请号：US17561480
申请日：2021-12-23
Applicant: Intel Corporation
Inventor： Patrick Hayes , Michael McCourt , Alexandra Johnson , George Ke , Scott Clark
Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.
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公开(公告)号：US11966860B2
公开(公告)日：2024-04-23
申请号：US17687362
申请日：2022-03-04
Applicant: Intel Corporation
Inventor： Kevin Tee , Michael McCourt , Patrick Hayes , Scott Clark
Abstract: Disclosed examples include after a first tuning of hyperparameters in a hyperparameter space, selecting first hyperparameter values for respective ones of the hyperparameters; generating a polygonal shaped failure region in the hyperparameter space based on the first hyperparameter values; setting the first hyperparameter values to failure before a second tuning of the hyperparameters; and selecting second hyperparameter values for the respective ones of the hyperparameters in a second tuning region after the second tuning of the hyperparameters in the second tuning region, the second tuning region separate from the polygonal shaped failure region.
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18.发明公开公开(公告)号：US20240127124A1
公开(公告)日：2024-04-18
申请号：US18397909
申请日：2023-12-27
Applicant: Intel Corporation
Inventor： Michael McCourt , Ben Hsu , Patrick Hayes , Scott Clark
IPC: G06N20/20 , G06F18/21 , G06F18/211 , G06F18/22 , G06F18/23
CPC classification number: G06N20/20 , G06F18/211 , G06F18/217 , G06F18/22 , G06F18/23
Abstract: Disclosed examples including generating a joint model based on first and second subject models, the first and second subject models selected based on a relationship between the first and second subject models; selecting the joint model from a plurality of joint models after a determination that entropy data points of the joint model satisfy a threshold, the entropy data points based on multiple tuning trials of the joint model; and providing tuning data associated with the joint model to a tuning session of a target model.
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公开(公告)号：US20220188677A1
公开(公告)日：2022-06-16
申请号：US17687362
申请日：2022-03-04
Applicant: Intel Corporation
Inventor： Kevin Tee , Michael McCourt , Patrick Hayes , Scott Clark
Abstract: Disclosed examples include after a first tuning of hyperparameters in a hyperparameter space, selecting first hyperparameter values for respective ones of the hyperparameters; generating a polygonal shaped failure region in the hyperparameter space based on the first hyperparameter values; setting the first hyperparameter values to failure before a second tuning of the hyperparameters; and selecting second hyperparameter values for the respective ones of the hyperparameters in a second tuning region after the second tuning of the hyperparameters in the second tuning region, the second tuning region separate from the polygonal shaped failure region.
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公开(公告)号：US20220107850A1
公开(公告)日：2022-04-07
申请号：US17516296
申请日：2021-11-01
Applicant: Intel Corporation
Inventor： Alexandra Johnson , Patrick Hayes , Scott Clark
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.
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