Hyperparameter Tuning Service Excluding Failure Regions

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Solution Overview

Problem

Existing machine learning models often lack optimal hyperparameters, leading to suboptimal predictive performance and computational inefficiencies, with existing optimization systems being complex and difficult to use, resulting in high computational costs and inefficiencies.

Innovation Solution

A distributed networked system with a machine learning-based tuning service that performs iterative hyperparameter tuning, identifying optimal values by excluding a defined failure region, and using an intelligent API to simplify the optimization process, enabling efficient and effective hyperparameter optimization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If existing hyperparameter optimization systems are used, then hyperparameter optimization can be achieved, but the interface complexity increases significantly and requires substantial coding capabilities and comprehension of underlying components

Engineering Contradiction:

Improvehyperparameter optimization accuracyVSAvoidsystem interface complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent introduces a cloud-based hyperparameter optimization service that acts as an intermediary between users and the complex optimization system. Users can submit optimization requests through a simplified interface, and the service handles the complex underlying computations, model training, and hyperparameter tuning automatically, eliminating the need for users to directly interact with complex software components

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The optimization service performs self-service by automatically executing the hyperparameter optimization process without requiring user intervention in the technical details. The system autonomously trains models, evaluates hyperparameter combinations, and selects optimal parameters based on predefined criteria, freeing users from needing to understand or manually control the optimization process

Inventive Principle:

Principle #25Self-service

2Manufacturing precision

If hyperparameter optimization is performed exhaustively, then optimal predictive performance can be achieved, but computational costs and time consumption increase significantly

Engineering Contradiction:

Improvepredictive performance accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

Instead of exhaustively testing all possible hyperparameter combinations, the patent employs partial action through intelligent sampling and evaluation. The system uses techniques such as random search, grid search with strategic sampling, and early stopping to evaluate only the most promising hyperparameter combinations, achieving good predictive performance without the computational cost of complete exhaustive search

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The optimization process incorporates feedback mechanisms where model performance metrics from previous evaluations guide subsequent hyperparameter selection. The system uses performance feedback to prioritize promising hyperparameter combinations and adjust the search strategy dynamically, reducing unnecessary computational exploration of unlikely candidates while maintaining high optimization effectiveness

Inventive Principle:

Principle #23Feedback

3Ease of manufacture

If default hyperparameters are used, then system implementation is simple, but predictive performance and computational efficiency are suboptimal

Engineering Contradiction:

Improvemodel implementation simplicityVSAvoidcomputational efficiency

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The system performs preliminary hyperparameter optimization before the actual machine learning task execution. By pre-optimizing hyperparameters using the cloud service, the system prepares efficiently configured models in advance, allowing the main computational tasks to run with optimal parameters set, thus improving productivity without complicating the primary implementation

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11966860B2Systems and methods implementing an intelligent machine learning tuning system providing multiple tuned hyperparameter solutions

Publication Date: 2024.04.23 INTEL CORP

AI summary

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.