Coefficient Conditioning for Multi-Task Models Without Full Retraining
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Solution Overview
Problem
Existing multi-task learning (MTL) systems require extensive training operations to determine coefficient trade-offs, which is computationally expensive and time-consuming, especially for large models.
Innovation Solution
A coefficient conditioning module (CCM) is introduced to generate an approximate Pareto-front, allowing for efficient determination of operational parameters for pre-trained machine learning models by minimizing expected loss functions over sampled weight vectors, reducing the need for full retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full retraining is performed to determine coefficient trade-offs for multi-task learning, then model performance and accuracy are improved, but computational cost and training time increase significantly
Solution Approach 1:
The patent pre-computes and stores coefficient trade-off curves during an initial phase, so that when new tasks need to be added, the system can directly query pre-computed results rather than performing full retraining. This preliminary computation of Pareto-optimal points for different task combinations eliminates the need for time-consuming full retraining operations.
Solution Approach 2:
The patent creates a simplified representation (copy) of the full training process by pre-computing coefficient trade-off curves that capture the essential relationships between tasks. This copy allows the system to make predictions about model performance for new task combinations without actually executing the full training process, thus reducing computational time while maintaining accuracy.
2Manufacturing precision
If full retraining is performed to determine coefficient trade-offs, then optimization accuracy is improved, but computational resources and costs increase
Solution Approach 1:
The system performs preliminary computation of coefficient trade-off curves during an initial training phase, storing the optimized coefficient values for different task combinations. When new tasks are introduced, the system queries these pre-computed results instead of performing full retraining, significantly reducing computational resource consumption while maintaining optimization accuracy.
Solution Approach 2:
The patent introduces an intermediary data structure (coefficient trade-off curves or lookup tables) that mediates between the full training process and new task requirements. This intermediary stores pre-computed optimal coefficients, allowing the system to answer new task configuration questions without re-executing the computationally intensive full training process, thus reducing energy consumption while preserving accuracy.
3Adaptability or versatility
If multiple training operations are performed to explore coefficient space, then model adaptability to different task combinations is improved, but system complexity and operational overhead increase
Solution Approach 1:
The system performs preliminary exploration of coefficient space by pre-computing trade-off curves for various task combinations during an initial phase. This preliminary action creates a ready-to-query database of optimal coefficients, enabling the system to adapt to new task combinations quickly through simple lookups rather than performing complex multiple training operations, thus reducing system operational complexity.
Solution Approach 2:
The patent creates a simplified data structure (copy) that represents the complex relationships between tasks and coefficients. This copy in the form of pre-computed trade-off curves allows the system to handle multiple task combinations without maintaining the full complexity of multiple training operations, thereby reducing operational overhead while preserving model adaptability.
Data Source
AI summary
Mechanisms are provided for reconfiguring a pre-trained ML model. Weight vectors are sampled from a pre-defined distribution to produce sampled weight vectors. A conditioning module is trained by minimizing a multi-task training loss over the sampled weight vectors, the multi-task training loss being a combination of task loss functions resulting from multiple tasks, respectively, each of the task loss functions being calculated by using a corresponding training dataset and a corresponding element of the respective sampled weight vector. The conditioning module includes computer executable logic implementing a function from the sampled weight vectors to a parameter space of a pre-trained machine learning model, the elements of the weight vector specifying a relative importance of a corresponding task. An operational parameter for the pre-trained machine learning model is determined by using the trained conditioning module, and the pre-trained machine learning model is reconfigured with the determined operational parameter.