Task-Specific Engine Training Using Embedding-Based Data Selection
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Solution Overview
Problem
Existing machine learning (ML) systems face issues of under-representation of input data and overfitting due to inadequate training sets, leading to suboptimal performance.
Innovation Solution
A method involving an embedding engine to identify and add or remove data elements based on the performance of the task-specific engine, using a low-dimensional representation to enhance training data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the training set includes a large amount of similar data, then the ML system may achieve better coverage of input types, but overfitting occurs which makes the ML system operate suboptimally
Solution Approach 1:
The patent implements feedback by evaluating the ML system's performance on the training set and using this performance information to dynamically adjust the training set composition. The system identifies poorly performing data elements and uses embedding similarity to find and add related data, creating a closed-loop feedback mechanism that continuously improves generalization while preventing overfitting.
Solution Approach 2:
The patent changes the parameters of the training set by dynamically adding or removing data elements based on performance metrics. The embedding dimension and similarity threshold are used as adjustable parameters to control the composition of the training set, allowing the system to optimize between coverage and generalization.
2Productivity
If the training set has holes where portions of input data are not well represented, then the ML system may operate efficiently on represented data, but it becomes poorly equipped to handle unseen input types
Solution Approach 1:
The patent performs preliminary action by proactively identifying gaps in the training set coverage and adding representative data before the ML system encounters unseen input types during deployment. The embedding-based similarity search enables the system to anticipate and prepare for potential input variations.
Solution Approach 2:
The patent introduces an intermediary mechanism (embedding engine) that bridges the gap between represented and unseen input types. By finding data elements with similar embedding representations, the system can infer and prepare for input types that are not directly represented in the training set.
3Measurement precision
If manual assessment of training data quality is performed, then precise identification of data elements can be achieved, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the mechanical manual assessment process with an automated computational system. The embedding engine and performance-based selection algorithm substitute human judgment, achieving precise identification of relevant data elements without manual intervention while significantly reducing time consumption.
Data Source
AI summary
A method and apparatus for training a task specific engine such that the task specific engine is first trained on set of labelled data. The labelled data is fed through the task specific engine once more. A performance score for each piece of data fed through the task specific engine is then generated. An embedding engine is then utilized to find further data that is similar to the pieces of labelled data with a low performance score. The further data is then labelled and used to further train the task specific engine with the aim of improving the overall performance of the task specific engine. Labelled data with high performance scores may also be grouped by the embedding engine and similar or duplicate data removed before further training the task specific engine on the reduced set of data.