Neural Network Weight Initialization for Non-Gaussian Data
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Solution Overview
Problem
Training deep neural networks for regression or classification tasks is lengthy and requires normalization of input data, which can be resource-intensive and slow down training when input data distributions deviate from a Gaussian distribution, especially in real-time applications with resource-constrained hardware.
Innovation Solution
A method for training deep neural networks that initializes model parameters using a transformation function to adapt weighting matrices and bias vectors based on the input data distribution, eliminating the need for normalization and accelerating training by optimizing initial conditions for non-Gaussian distributed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If input data is normalized using conventional methods (subtracting mean and dividing by standard deviation), then the training process converges faster for Gaussian-distributed data, but the computational overhead increases and training slows down for non-Gaussian distributed data
Solution Approach 1:
The patent changes the initialization parameters of the neural network weights and biases based on the actual distribution statistics (mean and standard deviation) of the input data. Instead of using fixed initialization values or values assuming Gaussian distribution, the method adapts the initialization parameters to match the actual data distribution, thereby eliminating the need for normalization and improving training efficiency for non-Gaussian data
Solution Approach 2:
The patent performs preliminary calculation of the input data distribution statistics (mean and standard deviation) before training initialization. This preliminary action allows the system to adapt the weight and bias initialization values in advance, avoiding the need for time-consuming normalization operations during the training process
2Ease of manufacture
If normalization is performed on input data sets, then model training can proceed with standard initialization methods, but the preprocessing step increases computational overhead and reduces training speed
Solution Approach 1:
The patent extracts the normalization operation from the training process by incorporating its effect directly into the weight and bias initialization. Instead of separately normalizing the data and then training, the method extracts the essential effect (adjusting for data distribution) and applies it during initialization, thereby eliminating the preprocessing step while maintaining training effectiveness
Solution Approach 2:
The patent merges the data normalization function with the weight initialization process. By combining these two operations into a single initialization step that accounts for input data distribution, the method eliminates the need for separate preprocessing while achieving the same effect, thus improving training speed without sacrificing simplicity
3Reliability
If random initialization from normalized Gaussian distribution is used, then training converges well for normalized input data, but performance degrades when input data distribution deviates from Gaussian
Solution Approach 1:
The patent introduces dynamic adaptation of initialization parameters based on the input data distribution. Instead of using fixed Gaussian initialization values, the method dynamically calculates the appropriate initialization parameters (mean and standard deviation for weights and biases) based on the actual distribution characteristics of the input data, thereby maintaining reliable convergence across diverse data distributions
Data Source
AI summary
A method is for training a data-based evaluation model for determining an evaluation result. The method includes providing training data sets that assign input data sets to one or more labels, and determining a distribution interval of values of all the input data sets. The method further includes performing an initial determination of model parameters for the data-based evaluation model as a function of the distribution interval, and training the data-based evaluation model with the training data sets by further adaptation of the model parameters.