Multi-Headed Machine Learning Model for Code Mapping
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Solution Overview
Problem
Traditional machine learning techniques for mapping third-party codes to third-party agnostic concepts suffer from biased training data, high training costs, and inefficiencies in handling dynamic code sets, leading to poor performance and hardware constraints.
Innovation Solution
A multi-headed machine learning model architecture with a gate function that routes text inputs through multiple processing pipelines, leveraging transfer training and knowledge distillation to train lightweight components based on heavyweight teacher models, allowing for efficient interpretation across incompatible datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning techniques use a single processing pipeline for all third-party codes, then the model structure is simple, but the performance is poor and biased training data occurs
Solution Approach 1:
The patent divides the single processing pipeline into multiple specialized attention heads, each dedicated to handling specific categories of third-party codes. This segmentation allows each attention head to be optimized for its specific code category, improving mapping accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
Different attention heads are assigned different qualities and specializations based on the specific code categories they handle. Each attention head has tailored parameters and processing characteristics optimized for its specific domain, enabling localized optimization of code mapping performance for different code types.
2Adaptability or versatility
If traditional machine learning techniques train models to handle all possible mapping scenarios, then comprehensive coverage is achieved, but training time and costs become excessively high
Solution Approach 1:
The training process is segmented into multiple stages: initial pre-training on a subset of data, followed by progressive fine-tuning on additional data. This allows the model to achieve comprehensive coverage through staged learning rather than requiring all data to be processed simultaneously, significantly reducing total training time.
Solution Approach 2:
The model performs preliminary training on a representative subset of code mapping scenarios to establish foundational capabilities. This preliminary action enables the model to handle common cases effectively before progressively learning more specialized mappings, avoiding the need to train on all possible scenarios from the outset.
3Power
If traditional machine learning techniques use heavy-weighted attention heads, then processing capability is sufficient, but hardware constraints cause large performance costs and low availability
Solution Approach 1:
The heavy processing load is segmented and distributed across multiple specialized attention heads rather than concentrated in a single heavy-weighted head. Each attention head handles a specific portion of the workload for its designated code category, reducing the computational burden on individual hardware components while maintaining overall processing capability.
Solution Approach 2:
Each attention head is optimized with appropriate weightings and parameters localized to its specific code category, avoiding the need for uniformly heavy-weighted heads across all categories. This localized optimization enables sufficient processing capability for each task while reducing overall hardware resource consumption compared to a single heavy-weighted approach.
Data Source
AI summary
Various embodiments of the present disclosure provide machine learning techniques for transforming third-party coding sets to universal canonical representations. The techniques may include receiving a plurality of training datasets corresponding to a plurality of predictive categories and generating a plurality of teacher models respectively corresponding to the plurality of predictive categories based on the plurality of training datasets. The techniques include generating a multi-headed composite model based on a plurality of trained parameters for each of the plurality of teacher models. The multi-headed composite model includes a plurality of model heads that respectively correspond to the plurality of teacher models and the plurality of predictive categories. The multi-headed composite model is leveraged to generate an output embedding for a text input of any predictive category. Each text input is processed by selecting a particular head of the multi-headed composite model that corresponds to the predictive category of the text input.