Neural Network Parameter Optimization via Equivalent Class Base Paths
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Solution Overview
Problem
Deep learning networks, especially deep neural networks, face inefficiencies in training due to redundant parameter spaces and the need for extensive computational resources, often resulting in slow convergence and suboptimal parameter optimization.
Innovation Solution
The optimization scheme involves representing learning networks in equivalent class space by determining base paths through multiple layers, where each node uses a scaling invariant activation function, allowing for linearly independent processing and updating combined parameter values to improve training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional parameter optimization methods are used to train deep neural networks, then the network can achieve accurate learning, but the training process consumes excessive computational resources and time
Solution Approach 1:
The patent segments the parameter optimization process into two distinct phases: (1) determining base paths through the network that capture essential parameter relationships, and (2) optimizing only the combined parameter values along these base paths. This segmentation reduces the optimization space from all parameters to a subset of combined parameter values, significantly decreasing computational complexity and training time while preserving optimization accuracy.
Solution Approach 2:
The patent transforms the optimization problem from the original parameter space to an equivalent class space defined by base paths. By changing the dimensionality and representation of parameters (from individual parameters to combined parameter values along paths), the optimization process operates in a reduced-dimensional space that maintains equivalence to the original problem, thereby reducing computational burden without sacrificing accuracy.
2Measurement precision
If traditional parameter optimization methods are used to train deep neural networks, then the network can achieve accurate learning, but the computational resource consumption is excessive
Solution Approach 1:
The patent segments the parameter optimization process into two distinct phases: (1) determining base paths through the network that capture essential parameter relationships, and (2) optimizing only the combined parameter values along these base paths. This segmentation reduces the optimization space from all parameters to a subset of combined parameter values, significantly decreasing computational complexity and training time while preserving optimization accuracy.
Solution Approach 2:
The patent transforms the optimization problem from the original parameter space to an equivalent class space defined by base paths. By changing the dimensionality and representation of parameters (from individual parameters to combined parameter values along paths), the optimization process operates in a reduced-dimensional space that maintains equivalence to the original problem, thereby reducing computational burden without sacrificing accuracy.
3Measurement precision
If extensive iteration processes are used to update parameters, then parameter optimization accuracy can be improved, but the convergence speed becomes slow
Solution Approach 1:
The patent segments the parameter optimization process into two distinct phases: (1) determining base paths through the network that capture essential parameter relationships, and (2) optimizing only the combined parameter values along these base paths. This segmentation reduces the optimization space from all parameters to a subset of combined parameter values, significantly decreasing computational complexity and training time while preserving optimization accuracy.
Solution Approach 2:
The patent performs preliminary action by determining base paths before the optimization process begins. These base paths pre-identify the essential parameter relationships and structures within the network, allowing the subsequent optimization to focus only on the relevant combined parameter values. This preliminary structuring accelerates convergence by avoiding unnecessary iterations over redundant parameters.
4Measurement precision
If all parameters are optimized independently, then comprehensive parameter optimization can be achieved, but the complexity of the optimization process increases
Solution Approach 1:
The patent merges individual parameter optimizations into combined parameter value optimizations along base paths. Instead of treating each parameter independently, the method combines parameters that lie along the same base path and optimizes their joint effect. This merging reduces the number of optimization variables and simplifies the optimization landscape while maintaining comprehensive coverage of parameter interactions.
Solution Approach 2:
The patent transforms the optimization problem from the original parameter space to an equivalent class space defined by base paths. By changing the dimensionality and representation of parameters (from individual parameters to combined parameter values along paths), the optimization process operates in a reduced-dimensional space that maintains equivalence to the original problem, thereby reducing computational burden without sacrificing accuracy.
Data Source
AI summary
In implementations of the present disclosure, a solution for optimization of a learning network in an equivalent class space is provided. In this solution, base paths running through layers of a learning network are determined. Each node utilizes an activation function with a scaling invariant property to process an input from a node of a previous layer, each base path comprises a single node in each layer, and processing in the base paths is linearly independent from each other. A combined value of parameters associated with nodes in each base path is updated. A parameter associated with a node is used to adjust an input obtained from a node of a previous layer. Values of parameters associated with nodes in the base paths are updated based on updated combined values of parameters. Through this solution, optimization efficiency can be improved and more accurate optimized values of parameters are achieved.