Gated Neural Network Units for Overfitting Reduction
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Solution Overview
Problem
Existing neural networks face challenges in efficiently weighting contributions of network units based on their impact, leading to suboptimal performance and overfitting.
Innovation Solution
The implementation of a gated network unit with a gating path that includes gating units, benefit paths, scaling functions, and clipping functions to weight and eliminate contributions from network units based on their benefit scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural networks weight all network unit contributions equally, then the network maintains simplicity in structure, but the network suffers from overfitting and suboptimal performance due to inability to selectively emphasize beneficial units
Solution Approach 1:
The patent segments the neural network into distinct components: network units that process data and gating units that control their contributions. Each network unit is paired with a gating unit, creating modular segments that can be independently trained and controlled. This segmentation allows the system to manage complexity by organizing weights into structured groups (network weights and gating weights) with clear functional responsibilities.
Solution Approach 2:
The gating units serve as intermediary components between network units and the final computation. Instead of directly weighting all network unit outputs, the gating units act as mediators that selectively modulate contributions based on learned gating weights. This intermediary layer resolves the contradiction by introducing controlled complexity that improves performance while maintaining architectural organization.
2Productivity
If the neural network includes all network units without selection, then the network maintains completeness of information processing, but the network consumes excessive computing resources and exhibits reduced reproducibility
Solution Approach 1:
The gating weights are trained preliminarily during the training phase to determine which network units should be active for specific input types. This preliminary action of learning gating weights allows the network to pre-establish which units are beneficial before actual inference, enabling selective activation that improves computing efficiency while maintaining reproducibility through consistent gating weight application.
Solution Approach 2:
The patent changes the parameter space by introducing gating weights as additional learnable parameters that control network unit activation. By optimizing these gating parameters during training, the system learns to dynamically adjust which network units contribute to the output, achieving better resource efficiency and reproducibility without sacrificing information completeness.
3Reliability
If the neural network uses uniform weighting for all network units, then the training process is computationally efficient, but the network fails to capture the varying impact of different network units leading to overfitting
Solution Approach 1:
The patent applies local quality by allowing different gating weights for different network units and different input types. Instead of a single uniform weighting scheme, each network unit can have its own gating weight that is optimized based on its specific contribution to the task. This local differentiation enables the network to capture varying impacts of different units, reducing overfitting while maintaining manageable complexity through structured weight organization.
Data Source
AI summary
Systems and methods according to the present disclosure can employ a computer-implemented method for inference using a machine-learned model. The method can be implemented by a computing system having one or more computing devices. The method can include obtaining data descriptive of a neural network including one or more network units and one or more gating paths, wherein each of the gating path(s) includes one or more gating units. The method can include obtaining data descriptive of one or more input features. The method can include determining one or more network unit outputs from the network unit(s) based at least in part on the input feature(s). The method can include determining one or more gating values from the gating path(s). The method can include determining one or more gated network unit outputs based at least in part on a combination of the network unit output(s) and the gating value(s).