Entangled Synapse Weight Update for Neural Network Training
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Solution Overview
Problem
Artificial neural networks face challenges in training due to high-dimensional, non-linear optimization problems, uncertainty in finding global optima, overfitting, and the need for extensive training data, which limits their efficiency and generalization capabilities.
Innovation Solution
The approach involves dividing synapses into entangled and unentangled subsets, where weight factors of entangled synapses are updated simultaneously using correlated random components, and those of unentangled synapses are updated individually with uncorrelated components, allowing for faster training with less data and reducing overfitting by introducing stochastic components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional training methods are used to train neural networks, then the network can learn from data, but the training process requires extensive training data and time-consuming optimization
Solution Approach 1:
The patent segments the synapses into multiple subsets and assigns different learning rates to each subset. This segmentation allows the network to learn different features at different speeds, improving training efficiency without sacrificing accuracy. The gradient computation is also segmented to focus on relevant features, reducing the time needed to process all parameters uniformly.
Solution Approach 2:
The patent implements dynamic learning rates that adapt during training based on the importance and difficulty of learning different synapse subsets. This dynamic adjustment allows the system to optimize training speed by allocating more computational resources to difficult-to-learn features while quickly mastering easier features, thereby reducing overall training time while maintaining accuracy.
2Measurement precision
If traditional training methods are used with extensive training data, then the network can achieve accurate results, but the data requirements and computational resources increase significantly
Solution Approach 1:
By segmenting synapses into subsets with different learning rates, the patent enables the network to learn more efficiently from limited data. The segmented approach allows the network to focus computational resources on the most informative features, extracting maximum learning value from smaller datasets rather than requiring extensive data to learn all features uniformly.
Solution Approach 2:
The patent changes the learning rate parameter dynamically for different synapse subsets, allowing the network to adapt its learning behavior based on feature importance. This parameter adjustment enables more effective learning from limited training data by allocating higher learning rates to critical features that provide the most information, reducing the overall data quantity needed for accurate learning.
3Adaptability or versatility
If the neural network is trained extensively to avoid overfitting, then generalization improves, but the training complexity and resource requirements increase
Solution Approach 1:
The patent segments the training process into multiple subsets with different learning rates, which naturally regularizes the learning process. This segmentation prevents any single subset from overfitting by distributing the learning burden across multiple groups, improving generalization while keeping the training process more manageable and less complex than training all parameters uniformly with extensive data.
Data Source
AI summary
A computer system implementing at least one neural network (1), a method for operating a computer system and a computer program for configuring such a computer system or for carrying out such a method in which at least a subset of synapses (3) of artificial neurons (2) of the at least one neural network (1) is defined as entangled synapses (3) the weight factors (w) of which are updated at the same time during a computational step on the basis of correlated random components and in which weight factors (w) of unentangled synapses (3) are updated individually on basis of uncorrelated random components.