Neural Network Parameter Approximation via Correlation Mode Decomposition
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Solution Overview
Problem
Training neural networks is computationally intensive, and existing methods fail to efficiently reduce dimensionality and improve generalization capacity, leading to suboptimal performance in machine learning tasks.
Innovation Solution
The Correlation Mode Decomposition (CMD) algorithm reduces dimensionality by grouping highly correlated network parameters into modes, allowing for efficient modeling of neural network dynamics and regularization, thereby improving generalization capacity and training acceleration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural network training methods are used, then the model can achieve accurate predictions, but the computational complexity and training time become excessively high
Solution Approach 1:
The patent segments the neural network parameters into distinct modes based on their correlation structures. By identifying and separating parameters that evolve similarly during training into the same mode, the method reduces the effective dimensionality of the parameter space. This segmentation allows for more efficient training by reducing redundant computations while preserving the essential dynamics needed for accurate predictions.
Solution Approach 2:
The patent transforms the parameter representation by introducing mode-based grouping and correlation analysis. Instead of treating all parameters independently, the method changes the parameter space organization to reflect temporal correlations, enabling dimensionality reduction through techniques like PCA applied to mode representations. This parameter transformation maintains predictive accuracy while reducing computational burden.
2Adaptability or versatility
If the neural network uses a large number of parameters, then the model capacity and representation power increase, but the dimensionality and computational requirements increase
Solution Approach 1:
The patent introduces a new dimension of analysis by examining parameter evolution over training time. By adding the temporal dimension to the parameter space and analyzing correlations across this dimension, the method identifies redundant degrees of freedom. This dimensional transformation enables reduction of the effective parameter count while preserving model capacity, as parameters that are highly correlated over time can be represented more efficiently.
Solution Approach 2:
The patent segments parameters into modes based on their temporal correlation patterns. This segmentation groups parameters that exhibit similar evolution trajectories, allowing the model to capture essential dynamics with fewer independent parameters. The segmentation approach maintains adaptability by preserving the distinct behavioral patterns of different parameter groups while reducing overall dimensionality.
3Manufacturing precision
If full parameter training is performed, then the model achieves optimal performance on training data, but generalization capacity is reduced due to overfitting
Solution Approach 1:
The patent extracts and analyzes the temporal correlation structure of parameter updates during training. By separating the correlated dynamics from the full parameter space, the method identifies which parameters are truly independent and which are redundant. This extraction approach enables regularization by preventing the model from fitting noise in redundant parameter dimensions, thereby improving generalization while maintaining training performance.
Solution Approach 2:
The patent incorporates feedback from the observed parameter dynamics during training. By monitoring how parameters evolve and correlate over time, the method adapts the training process to account for these correlations. This feedback mechanism enables the system to adjust learning rates or regularization strengths based on the actual parameter behavior, preventing overfitting while maintaining optimal training performance.
Data Source
AI summary
A method and system for implementing a machine-learning (ML) based function may include providing a NN model comprising a plurality of NN parameters; training the NN model over a plurality of training epochs, to implement a predefined ML function, based on a training dataset; for one or more NN parameters of the plurality of NN parameters: (i) calculating a profile vector, representing evolution of the NN parameter through the plurality of training epochs; and (ii) calculating an approximated value of the at least one NN parameter, based on the profile vector; and replacing at least one NN parameter value in the trained NN model with a respective calculated approximated value, to obtain an approximated version of the trained NN model.