Ladder-Type Neural Network Structure for Efficient Training
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Solution Overview
Problem
Existing neural network structures face challenges in efficient training, particularly in unsupervised learning, where context connections lead to computational demands and difficulties in learning abstract features, and are not well-compatible with supervised learning.
Innovation Solution
A neural network structure comprising a base layer with a corruption function, decoding function, and cost function, and additional layers with encoding and decoding functions, where corrupted input data is fed through encoding and decoding paths with lateral connections, and a total cost function is minimized to optimize parameter adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context connections are used to guide learning in neural networks, then learning efficiency is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the neural network into distinct functional layers: a base layer that processes bottom-up information and higher layers that process top-down context information. Each layer has separate encoding and decoding functions, allowing independent optimization and reducing the computational burden on any single component while maintaining the benefits of contextual guidance.
Solution Approach 2:
The patent introduces intermediate representations at each layer that act as mediators between bottom-up sensory information and top-down contextual information. These intermediate representations allow the network to integrate multiple information sources without requiring direct computation between all units, thereby reducing overall computational complexity while preserving learning efficiency.
2Loss of information
If de-correlation is applied between all processing units in a large network, then information redundancy is reduced, but computational demand becomes impractical
Solution Approach 1:
The patent applies segmentation by organizing processing units into hierarchical layers where de-correlation constraints are applied locally within each layer rather than globally across all units. This layered approach maintains information diversity while reducing the computational complexity from O(N²) to O(N×L) where N is the number of units and L is the number of layers.
Solution Approach 2:
The patent implements partial de-correlation by applying constraints only to specific connections between layers rather than all possible connections. This selective application of de-correlation reduces computational demand while still effectively reducing information redundancy in the most critical pathways.
3Measurement precision
If multiple cost functions are used at different network levels, then learning precision is improved, but training time increases
Solution Approach 1:
The patent segments the cost function into layer-specific components that can be computed and minimized independently at each hierarchical level. This allows parallel computation of multiple cost functions during training, reducing overall training time while maintaining the precision benefits of multi-level optimization.
Solution Approach 2:
The patent ensures continuous useful action by designing the cost functions to work together in a cohesive optimization process rather than as separate, redundant evaluations. The cost functions are integrated into a unified training framework that propagates gradients efficiently through the network, maintaining learning precision while minimizing redundant computations.
Data Source
AI summary
Disclosed is a neural network structure enabling efficient training of the network and a method thereto. The structure is a ladder-type structure wherein one or more lateral input(s) is/are taken to decoding functions. By minimizing one or more cost function(s) belonging to the structure the neural network structure may be trained in an efficient way.