Directed Acyclic Graph Neural Network Branching for Parallel Model Training
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Solution Overview
Problem
Existing machine-learning model development is time-consuming and labor-intensive, requiring manual setup and tuning of neural network models for accurate data recognition and classification, which hinders efficient model comparison and selection.
Innovation Solution
A computer-implemented method and system that utilize a deep learning architecture represented as a directed acyclic graph, allowing for the simultaneous training and comparison of multiple machine-learning models with different optimizer nodes and hyper-parameters, enabling parallel development and testing of various models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setup and tuning of neural network models is performed to achieve accurate data recognition and classification, then model accuracy is improved, but development time and labor intensity increase
Solution Approach 1:
The patent segments the model development process by dividing it into distinct components: automatic model selection, hyperparameter optimization, and parallel training execution. This segmentation allows each component to be optimized independently while maintaining overall system efficiency, resolving the contradiction between accuracy and development time.
Solution Approach 2:
The system implements self-service through automatic model selection and hyperparameter tuning mechanisms that eliminate manual intervention. The framework autonomously selects appropriate models and configures optimal parameters based on the problem characteristics, thereby maintaining high accuracy while dramatically reducing development time and labor intensity.
2Measurement precision
If multiple machine-learning models are trained and compared to find the optimal model, then model selection quality is improved, but computing resources and time consumption increase
Solution Approach 1:
The patent applies partial action by training and comparing only a subset of relevant models rather than exhaustively evaluating all possible models. The system intelligently identifies and evaluates only those models most likely to perform well based on problem characteristics, thereby improving model selection quality while consuming fewer computing resources.
Solution Approach 2:
The framework merges multiple model training processes into a unified parallel execution environment. By combining resource management, data handling, and evaluation metrics into a single integrated system, the patent enables efficient comparison of multiple models while optimizing resource utilization and reducing overall computing overhead.
3Adaptability or versatility
If manual setup process is used for machine-learning model development, then model customization is improved, but development efficiency decreases
Solution Approach 1:
The patent implements dynamics by providing a flexible framework that adapts to different problem types and requirements. The system dynamically selects appropriate models and configurations based on input data characteristics, maintaining high customization capability while automating the development process to improve efficiency.
Solution Approach 2:
The framework achieves universality by designing a multi-functional platform that handles various model types, optimization algorithms, and evaluation metrics within a single system. This universal approach enables automated customization across different applications, thereby maintaining adaptability while significantly improving development efficiency through elimination of manual setup processes.
Data Source
AI summary
Embodiments of the present invention enable a comparison of different machine-learning models based on a single neural network design may be provided. A deep learning architecture for an experimentation framework is represented as a directed acyclic graph with nodes representing neural network layers. Embodiments of the present invention specify a first machine-learning model in a first branch and a second machine-learning model in a second branch of the directed acyclic graph. Each branch has its own optimizer node. Embodiments of the present invention generate source code for the first machine-learning model and the second machine-learning model out of the directed acyclic graph, and train the first machine-learning model and the second machine-learning model simultaneously, thereby enabling the comparison of different machine-learning models.