Self-Stabilized Deep Neural Network Gradient Control
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Solution Overview
Problem
Training deep neural networks is challenging due to the gradient vanishing problem, which leads to high computational overhead and inefficient training processes, especially with the need for unsupervised pre-training and complex parameter initialization.
Innovation Solution
A neural network with level-specific stabilization parameters adjusts the learning rate at each level during training, incorporating a linear bypass connection and per-layer stabilizers to stabilize the gradient and reduce the exponential decay of error gradients, allowing for faster convergence and reduced computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks with many layers are used to model complex relationships, then the network's learning capacity and representation ability improve, but the gradient vanishes exponentially during training, making training difficult
Solution Approach 1:
The patent divides the deep neural network into multiple levels (input level, intermediate levels, output level) and applies different stabilization parameters to each level. This segmentation allows independent control of gradient flow at different depths, preventing exponential vanishing while maintaining the network's deep structure and learning capacity.
Solution Approach 2:
The patent introduces level-specific stabilization parameters that are applied locally at each level of the neural network. Each level has its own stabilization parameter that adjusts the gradient magnitude locally, allowing the network to maintain stability throughout its depth while preserving the benefits of having many layers for complex representation.
2Ease of manufacture
If traditional back propagation is used to train deep networks, then the training process follows standard procedures, but unsupervised pre-training and complex parameter initialization are required, increasing computational overhead
Solution Approach 1:
The stabilization parameters are learned automatically during the training process through back propagation, eliminating the need for manual parameter initialization or separate pre-training phases. The network self-adjusts its gradient flow characteristics, making the training process simpler and more self-contained.
Solution Approach 2:
The patent introduces stabilization parameters as additional trainable parameters in the network. These parameters are optimized during standard back propagation training, allowing the network to adapt gradient flow dynamically without requiring complex pre-training procedures or specialized initialization schemes.
3Measurement precision
If the number of levels in the neural network is increased to improve pattern recognition accuracy, then the network can represent function classes better, but the training time and computational resources required increase significantly
Solution Approach 1:
By segmenting the deep network into levels with individual stabilization parameters, the patent enables more efficient gradient flow through each layer. This allows training of deeper networks (with more levels for higher accuracy) to converge faster, reducing the training time penalty associated with increased network depth.
Data Source
AI summary
A neural network is structured with a plurality of levels of nodes. Each level has a level-specific stabilization parameter that adjusts a learning rate, at a corresponding level, during training. The stabilization parameter has a value that varies inversely relative to a change in an objective training function during back-propagation of the error through the level.