公开(公告)号：US12033036B2

公开(公告)日：2024-07-09

申请号：US16943643

申请日：2020-07-30

Applicant: Intel Corporation

Inventor： Michael McCourt ,  Bolong Cheng ,  Taylor Jackie Spriggs ,  Halley Vance ,  Olivia Kim ,  Ben Hsu ,  Sarth Frey ,  Patrick Hayes ,  Scott Clark

IPC: G06N20/00 ,  G06F9/54 ,  G06F18/2115 ,  G06F18/214 ,  G06N20/20

CPC classification number: G06N20/00 ,  G06F9/541 ,  G06F18/2115 ,  G06F18/2148 ,  G06N20/20

Abstract: Systems and methods for tuning hyperparameters of a model include receiving a tuning request for tuning hyperparameters, the tuning request includes a first and a second objective function for the machine learning model. The first and second objective functions may output metric values that do not improve uniformly. Systems and methods additionally include defining a joint tuning function that is based on a combination of the first and second objective functions; executing a tuning operation; identifying a Pareto efficient frontier curve defined by a plurality of distinct hyperparameter values; applying metric thresholds to the Pareto efficient frontier curve; demarcating the Pareto efficient frontier curve into at least a first infeasible section and a second feasible section; searching the second feasible section of the Pareto efficient frontier curve for one or more proposed hyperparameter values; and identifying at least a first set of proposed hyperparameter values based on the search.

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.

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

IPC: G06N7/00 ,  G06N20/00

CPC classification number: G06N7/00 ,  G06N20/00

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.

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.

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

IPC: G06N7/00 ,  G06N20/00

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.

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

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.

公开(公告)号：US12236287B2

公开(公告)日：2025-02-25

申请号：US18326467

申请日：2023-05-31

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.

公开(公告)号：US20230385129A1

公开(公告)日：2023-11-30

申请号：US18326467

申请日：2023-05-31

Applicant: Intel Corporation

Inventor： Alexandra Johnson ,  Patrick Hayes ,  Scott Clark

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

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

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.

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.

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.