Scalable Autonomous Coding With Hierarchical Models for Rare Codes
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Solution Overview
Problem
Traditional machine learning models for autonomous coding in complex prediction spaces face challenges due to scalability issues and performance degradation with rare codes, while rule-based models lack adaptability and require static rule definitions.
Innovation Solution
A multi-layered machine learning model architecture with hierarchical layers and residual connections that share information across layers, using a Mixture of Experts (MoE) approach to enhance model performance by leveraging both probabilistic and deterministic outputs, and a staged training technique to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are used for autonomous coding in large prediction spaces, then scalability is improved, but performance deteriorates for rare codes due to insufficient training data
Solution Approach 1:
The patent combines multiple machine learning models into an ensemble architecture where each model specializes in different code frequency categories (common, uncommon, rare codes). This merging allows the system to leverage the scalability of machine learning while maintaining high accuracy for rare codes through specialized models trained on appropriate data subsets.
Solution Approach 2:
Different models within the ensemble have different training data compositions tailored to specific code frequency ranges. Models for rare codes are trained with synthetic data and transfer learning techniques specifically optimized for low-frequency codes, while common code models use standard training data, creating local optimization for different prediction scenarios.
2Measurement precision
If rule-based models are used for autonomous coding, then accuracy is improved for specific codes, but adaptability deteriorates due to rigidity and lack of scalability
Solution Approach 1:
The patent replaces traditional rule-based mechanical systems with machine learning models that automatically learn patterns from data. The ensemble architecture further refines this by using multiple specialized ML models instead of a single rigid rule system, enabling both high accuracy and adaptability through data-driven predictions rather than static rules.
Solution Approach 2:
The system dynamically selects which model to use based on code frequency characteristics of the input. This dynamic adaptation allows the system to switch between different modeling approaches (common code models, uncommon code models, rare code models) depending on the specific prediction task, providing both accuracy and versatility.
3Measurement precision
If transfer learning techniques are applied to improve machine learning performance, then performance is improved for codes with insufficient training data, but accuracy deteriorates for codes with robust training data
Solution Approach 1:
The patent segments the code prediction space into distinct frequency categories (common, uncommon, rare) and creates separate specialized models for each segment. This segmentation prevents transfer learning from degrading common code predictions because common code models are trained independently on sufficient data without being influenced by transfer learning techniques designed for rare codes.
Solution Approach 2:
Transfer learning and synthetic data techniques are applied locally only to models handling rare and uncommon codes, while common code models use standard training procedures. This localized application of transfer learning maintains high reliability for common codes while improving accuracy for rare codes.
4Adaptability or versatility
If the number of codes and interconnectivity nuances increase within prediction space, then comprehensiveness is improved, but model complexity and maintenance requirements increase
Solution Approach 1:
The patent segments the complex prediction space into frequency-based categories and creates specialized models for each segment. This segmentation simplifies the overall system architecture by breaking down the complex task of handling all codes into manageable sub-tasks, each handled by optimized models with appropriate complexity levels.
Solution Approach 2:
The ensemble architecture provides a universal framework that handles all code types through a standardized multi-model structure. This universal approach simplifies maintenance by providing consistent interfaces and workflows across different code frequency categories, despite the specialized nature of individual models.
Data Source
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AI summary
Various embodiments of the present disclosure provide machine learning architectures and training techniques for improving predictive functionality of a computer. The techniques apply a multi-layered machine learning model to a target prediction domain to generate a model prediction for an input data object. The techniques may include inputting a vector to layer models of the multi-layered machine learning model to generate a layer code predictions for a code defined within a target coding domain. The techniques include inputting the layer code predictions to a layer metamodels of the multi-layered machine learning model to generate intermediate code predictions for the code. The techniques include inputting intermediate outputs to a fusion model of the multi-layered machine learning model to generate a fused code prediction for the code and outputting a model prediction for the code based on the fused code prediction.