Machine Learning Model Domain Index Matrix for Multi-Distribution Data
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Solution Overview
Problem
Machine learning models trained on multiple data distributions with differing attributes often generate inaccurate results when trying to approximate an average of ground-truth data, as they fail to explicitly distinguish between the attributes of different training data sets, leading to inconsistent performance across conditions.
Innovation Solution
A machine learning model is trained to use a domain index matrix that specifies which training data distribution to use for each input, allowing it to generate outputs with specific attributes by controlling the influence of each training data distribution, enabling it to handle noise and aggregate information from multiple distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a machine learning model is trained on multiple data distributions with differing attributes, then the model can handle diverse input conditions, but the model generates inaccurate results when trying to approximate an average of ground-truth data
Solution Approach 1:
The patent segments the training process by introducing domain index matrices that separate and identify different data distributions within the combined training data. Each domain index matrix element corresponds to a specific data distribution, allowing the model to segment and selectively apply knowledge from appropriate distributions during inference, thereby resolving the conflict between handling diversity and maintaining accuracy.
Solution Approach 2:
The patent introduces domain index matrices as intermediary structures that mediate between the multiple data distributions and the machine learning model. These matrices serve as control signals that guide the model to selectively utilize information from specific distributions, enabling accurate approximation of ground-truth data while maintaining the ability to handle diverse conditions.
2Adaptability or versatility
If multiple training data distributions are used to improve model performance, then the model can generate outputs with varying attributes, but the device requires more memory resources
Solution Approach 1:
The patent merges multiple data distributions into a single combined training dataset while introducing domain index matrices to track and control the origin of each data point. This merging approach allows the model to learn from diverse distributions without requiring separate model instances for each distribution, significantly reducing memory requirements while maintaining the ability to generate outputs with varying attributes.
Solution Approach 2:
The patent creates a universal machine learning model that can handle multiple data distributions through the use of domain index matrices. This single model structure serves multiple functions by selectively processing inputs from different distributions, eliminating the need for multiple specialized models and reducing overall memory consumption.
3Measurement precision
If multiple training data distributions are used to improve model accuracy, then the model can control output attributes, but the model structure becomes more complex
Solution Approach 1:
The patent uses domain index matrices as intermediary control structures that add minimal complexity to the model architecture. These matrices serve as simple lookup tables or control signals that guide the model's processing without requiring complex modifications to the core model structure, thereby maintaining relatively simple architecture while improving output accuracy.
Solution Approach 2:
The patent controls model behavior by changing parameter selections (domain index values) rather than modifying the model structure itself. This approach allows the model to adapt to different data distributions and control output attributes through parameter selection, avoiding the need for complex structural changes while maintaining high accuracy.
Data Source
AI summary
A method includes obtaining an input matrix and determining a domain index matrix that includes, for each respective input value of the input matrix, a corresponding domain index value that indicates a corresponding training data distribution of a plurality of training data distributions. The method also includes providing the input matrix and the domain index matrix to a machine learning model that has been trained using the plurality of training data distributions, where each respective training data distribution is associated with a different attribute. The method further includes generating, by the machine learning model and based on the input and the domain index matrices, an output matrix that includes, for each respective input value, a corresponding output value generated based on (i) the respective input value and (ii) the corresponding domain index value such that the corresponding output value exhibits the attribute of the corresponding training data distribution.