Neural Network Topology Determination via Probabilistic Graphical Models
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Solution Overview
Problem
Current methods for determining artificial neural network topologies are inefficient and unreliable, often requiring excessive computational resources, manual optimization, and labeled data, leading to sub-optimal performance and reduced accuracy.
Innovation Solution
The use of probabilistic graphical models to determine neural network topologies by bootstrapping a graph into a multi-graphical model or graphical model tree, allowing for unsupervised learning and customization based on datasets, enabling efficient and accurate optimization of neural network structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual optimization and exhaustive trial and error are used to determine neural network topologies, then customization and adaptability are improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The system performs self-service by automatically determining neural network topologies through probabilistic graphical models without requiring manual optimization. The logic autonomously analyzes datasets, constructs graphical models, and generates optimized topologies, eliminating the need for human operators to perform exhaustive trial and error while maintaining high adaptability to different datasets and tasks.
Solution Approach 2:
The patent replaces the mechanical manual optimization process with an automated computational system based on probabilistic graphical models. Instead of human operators manually adjusting and testing different topologies, the system uses algorithmic processes to automatically determine optimal structures, substituting human mechanical work with automated intelligent computation.
2Reliability
If exhaustive evaluation of all possible structures is performed, then reliability of topology determination is improved, but use of energy and computational resources worsen
Solution Approach 1:
The system segments the complex task of determining neural network topologies into distinct modular steps: constructing probabilistic graphical models from datasets, evaluating candidate topologies through model scoring, and selecting optimal structures. This segmentation allows the system to process information efficiently at each stage without requiring exhaustive evaluation of all possible topologies, reducing computational resource consumption while maintaining reliability.
Solution Approach 2:
The system performs preliminary action by constructing probabilistic graphical models from datasets before evaluating specific neural network topologies. This preliminary modeling phase captures the essential relationships and patterns in the data, enabling more efficient and accurate topology determination in subsequent steps without requiring exhaustive search of all possible structures.
3Productivity
If generic topologies are utilized instead of customized structures, then productivity is improved, but measurement precision and performance deteriorate
Solution Approach 1:
The system changes parameters by adapting neural network topology structures based on dataset characteristics and task requirements. Instead of using fixed generic topologies, the system modifies architectural parameters such as layer configurations, connection patterns, and node arrangements to optimize performance for specific applications, achieving both high productivity through automation and high precision through customization.
4Adaptability or versatility
If reinforcement learning or evolutionary algorithms are used to evaluate all possible structures, then adaptability is improved, but loss of time and computational resources worsen
Solution Approach 1:
The patent substitutes reinforcement learning and evolutionary algorithms with probabilistic graphical model-based determination. Instead of using time-consuming iterative learning processes that require extensive training data and computational resources, the system employs probabilistic reasoning and graphical model inference to directly determine optimal topologies, maintaining adaptability while dramatically reducing training time and resource consumption.
Data Source
AI summary
Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.