Machine Learning Model Learning Rate Adjustment via Dual Prediction
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Solution Overview
Problem
Existing methods for setting the learning rate in machine learning models are often heuristic or rely on computationally expensive search methods, leading to unsatisfactory results and poor performance.
Innovation Solution
A method that involves extracting multiple parameters from a target machine learning model, including the learning rate, state information, loss value, gradient, and weight, and using two machine learning models (a reinforcement learning model and a neural network model) to predict and adjust the learning rate based on minimizing loss values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If heuristic or simple search methods are used to set the learning rate, then the method is simple and easy to implement, but the training speed, quality, and generalization ability of the machine learning model are unsatisfactory
Solution Approach 1:
The patent introduces an intermediary system (the learning rate adjustment mechanism using multiple ML models including reinforcement learning and neural network models) that mediates between the simple heuristic methods and the complex optimal learning rate selection. This intermediary automatically predicts and selects optimal learning rates based on model state, bridging the gap between simplicity and performance without requiring manual complex tuning.
Solution Approach 2:
The machine learning model performs self-service by using its own state information (loss value, gradient, weight, etc.) as input to predict its own optimal learning rate. The reinforcement learning model and neural network model work together to enable the system to automatically adjust its own training parameters without external intervention, improving both performance and automation.
2Reliability
If computationally expensive search methods are used to find optimal learning rate, then the model performance can be improved, but the training time and computational resources increase significantly
Solution Approach 1:
The patent employs preliminary action by pre-training the reinforcement learning model and neural network model on historical training data and patterns. These pre-trained models can then quickly predict optimal learning rates during actual training without requiring expensive real-time search, having already learned optimal strategies in advance.
Solution Approach 2:
The patent substitutes the mechanical brute-force search method with an intelligent prediction system based on reinforcement learning and neural networks. Instead of systematically trying many learning rates (mechanical search), the system uses learned patterns to directly predict optimal values, replacing computational exhaustion with intelligent inference.
3Device complexity
If a single machine learning model is used to predict learning rate, then the system is simpler, but the accuracy and reliability of learning rate prediction is insufficient
Solution Approach 1:
The patent merges multiple machine learning models (reinforcement learning model and neural network model) into a unified prediction system. Each model contributes different strengths - the reinforcement learning model handles sequential decision-making while the neural network model captures complex patterns - and their combined predictions provide more accurate and reliable learning rate recommendations than any single model alone.
Solution Approach 2:
The patent creates a composite prediction system by combining different types of machine learning models, analogous to composite materials in engineering. The reinforcement learning component and neural network component work together as a composite structure, leveraging the strengths of each approach to achieve superior prediction accuracy compared to homogeneous single-model systems.
Data Source
AI summary
Embodiments of the present disclosure relate to a method for generating a machine learning model. The method includes extracting multiple parameters from a target machine learning model, where the multiple parameters include a learning rate, state information, a loss value, a gradient, and a weight, and the target machine learning model is configured to execute tasks related to at least one of images, videos, voice, and text. The method further includes predicting a first learning rate by a first machine learning model based on the multiple parameters; predicting a second learning rate by a second machine learning model based on the multiple parameters; choosing, based on the first learning rate and the second learning rate, a learning rate having a minimum loss value in the first learning rate and the second learning rate; and adjusting the target machine learning model based on the learning rate having the minimum loss value.