Encoder Regularization for Missing Data Stream Robustness
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Solution Overview
Problem
Machine learning models trained on multiple data streams can produce skewed predictions when certain streams are unavailable due to technical errors or policy-based restrictions, leading to incorrect outputs.
Innovation Solution
A training loss function is designed to regularize machine learning models by increasing the loss function value when outputs from complete and restricted data streams differ, and decreasing it when they are similar to those of other users, using encoder models to generate user-specific vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the model is trained on multiple data streams including restricted streams, then the predictive accuracy improves, but the model produces skewed outputs when those streams are unavailable
Solution Approach 1:
The patent modifies the training parameters by introducing a regularization term in the loss function that penalizes deviations between predictions made with complete data and predictions made with restricted data. This parameter change enables the model to learn representations that are robust to data stream availability while maintaining high predictive accuracy when all data is present.
Solution Approach 2:
The patent performs preliminary training actions by exposing the model to both complete and restricted data streams during the training phase. This preliminary exposure allows the model to adapt to varying data availability conditions before deployment, ensuring reliable performance whether restricted streams are available or not.
2Adaptability or versatility
If the model uses all available data streams for training, then the model complexity increases, but the model becomes more adaptable to different data conditions
Solution Approach 1:
The patent segments the training process into two distinct components: training with complete data streams and training with restricted data streams. This segmentation allows the model to learn different aspects of the problem separately, improving adaptability without requiring a proportionally larger overall model complexity.
Solution Approach 2:
The patent creates a universal model architecture that can handle multiple data stream conditions (complete data, restricted data, different user profiles) using the same underlying structure. The regularization mechanism enables this single model to serve multiple functions across different data availability scenarios.
3Productivity
If the loss function focuses on complete data streams, then the training efficiency improves, but the model performance degrades when data streams are restricted
Solution Approach 1:
The patent applies partial action by using only the necessary subset of data streams (unrestricted streams) when restricted streams are unavailable, rather than requiring all possible data. The loss function is designed to work effectively with this partial data input, maintaining training efficiency while ensuring acceptable prediction accuracy under restricted conditions.
Data Source
AI summary
In some embodiments, a method and related system for creating a steam-adaptable encoder model includes generating a first complete encoded representation using the encoder model based on unrestricted data provided by a first unrestricted data stream and restricted data provided by a restricted data stream. The method also includes generating an unrestricted encoded representation based on the first unrestricted data, evaluating a loss function value by updating the loss function value based on a similarity between the first complete encoded representation and the unrestricted encoded representation, and updating the encoder model based on the loss function value. The method also includes updating the encoder model based on the loss function value, generating an encoded representation using the encoder model based on filtered data that includes additional data from the first unrestricted data stream, and generating an indicator based on the encoded representation.