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：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.

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.

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.

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：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.

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.