Personalized Model Adapters Mixing Pre-trained Components
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Solution Overview
Problem
Existing machine learning model architectures are large and require vast amounts of training data and computational resources, making them inefficient for personalized tasks.
Innovation Solution
The method involves generating personalized model adapters by mixing pre-trained model adapters using a smaller set of enrollment data, rather than training new adapters from scratch, thereby reducing computational expense and data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained from scratch using vast amounts of training data, then model accuracy and reliability are improved, but computational expense and data requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training multiple adapters on different datasets before deployment. These pre-trained adapters are then combined using enrollment data to create a personalized model without requiring extensive training from scratch, thus reducing computational expense while maintaining accuracy
Solution Approach 2:
The patent merges multiple pre-trained adapters into a single personalized adapter by combining their parameters weighted by similarity scores. This merging process allows the system to leverage knowledge from multiple sources without retraining from scratch, reducing computational requirements while improving reliability
2Reliability
If machine learning models are trained from scratch using vast amounts of training data, then model accuracy and reliability are improved, but data requirements increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training adapters on diverse datasets beforehand. During personalization, only a small enrollment dataset is needed to determine adapter combinations, dramatically reducing the data requirement at deployment while maintaining model accuracy through the pre-acquired knowledge in the adapters
Solution Approach 2:
The patent creates copies of adapter parameters from pre-trained models and combines them to form the personalized model. This copying approach allows the system to reuse proven adapter configurations without requiring extensive new training data, thus reducing data requirements while preserving accuracy
3Productivity
If pre-trained model adapters are mixed using a smaller set of enrollment data, then computational expense and data requirements are reduced, but model personalization effectiveness must be maintained
Solution Approach 1:
The patent implements feedback by calculating similarity scores between the enrollment data and each pre-trained adapter, then using these scores to weight the contribution of each adapter to the final personalized model. This feedback mechanism ensures that the most relevant adapters are prioritized, maintaining personalization effectiveness while improving efficiency
Solution Approach 2:
The system changes parameters by adjusting the weights of adapter parameters based on similarity scores. This parameter adjustment allows the model to adapt to the specific user's preferences and behavior patterns from the enrollment data, maintaining personalization effectiveness while using only a small dataset for efficient computation
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A machine learning model is accessed. And an enrollment dataset for a device is accessed. A personalized model adapter generated based on the enrollment dataset and a plurality of model adapters is accessed. An input to the machine learning model is processed using the machine learning model in conjunction with the personalized model adapter. An output is provided, by the device, based on the processing.