Context Autoencoder Training for Unknown Streaming Data Contexts
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Solution Overview
Problem
Machine learning models face challenges in online training for streaming data due to changing contexts and unknown possible contexts, leading to underperforming models and false positives, especially in high-confusion regimes.
Innovation Solution
A method and system for context-based training of machine learning models that involve evaluating classification accuracy, applying training data to a knowledge bank of autoencoders, determining out-of-context data, constructing new context autoencoders, and initializing them, allowing for adaptive learning without prior knowledge of all contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If online training is performed for streaming data in real time, then the model can adapt to current data patterns, but the model performance deteriorates due to changing contexts and contradictory evidence in high-confusion regimes
Solution Approach 1:
The patent introduces context as an intermediary variable that mediates between input data and model predictions. By explicitly modeling context and using it to condition the model, the system can adapt to changing data patterns while maintaining reliability through context-aware decision making. The context acts as a mediator that resolves contradictions in high-confusion regimes by providing additional discriminative information.
Solution Approach 2:
The patent employs dynamic context identification and adaptation mechanisms that allow the model to change its behavior based on the current context. The context model is updated dynamically as new data arrives, enabling the system to adapt to evolving data distributions while maintaining performance through context-specific learning.
2Adaptability or versatility
If the number of possible contexts is not known in advance, then the system can handle unknown scenarios, but the complexity of identifying and managing contexts increases
Solution Approach 1:
The patent implements self-service context identification where the system automatically discovers and learns contexts from the data itself without requiring external specification. The model autonomously identifies context patterns and adapts to them, reducing the burden of manual context management while handling unknown scenarios effectively.
Solution Approach 2:
The system uses feedback mechanisms to continuously monitor data patterns and automatically adjust context identification. Through feedback from data performance and context relevance, the system refines its understanding of contexts over time, managing complexity through adaptive learning rather than exhaustive pre-specification.
3Reliability
If contextual features are hand-crafted or pre-defined, then reliable models can be learned in offline training, but the system cannot adapt to new contexts that were not anticipated
Solution Approach 1:
The patent performs preliminary context identification and modeling during offline training phases, establishing a foundation of known contexts and patterns. This preliminary action enables reliable baseline performance while the system retains the capability to discover and adapt to new contexts online, combining the benefits of pre-defined reliability with flexible adaptability.
Data Source
AI summary
According to an embodiment of the present disclosure, a method of training a machine learning model is provided. Input data is received from at least one remote device. A classifier is evaluated by determining a classification accuracy of the input data. A training data matrix of the input data is applied to a selected context autoencoder of a knowledge bank of autoencoders including at least one context autoencoder and the training data matrix is determined to be out of context for the selected autoencoder. The training data matrix is applied to each other context autoencoder of the at least one autoencoder and the training data matrix is determined to be out of context for each other context autoencoder. A new context autoencoder is constructed.