Conditional Neural Network Layer Weights via Latent Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional neural networks face limitations in dynamically determining layer weights, leading to restricted model complexity and increased computational resource consumption, which hinders their accuracy and efficiency in processing inputs.
Innovation Solution
The implementation of conditional neural network layers that dynamically determine conditional layer weights using differentiable mappings and latent parameters, allowing for a continuous range of weight values and increased representational capacity without significantly increasing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional neural networks use fixed layer weights, then the model structure is simple and computational resources are reduced, but the model complexity is restricted and accuracy is hindered
Solution Approach 1:
The patent applies dynamics by transitioning from fixed weights to dynamically selected weights. The network structure remains static, but the weights become dynamic variables that are selected based on the input data through gating mechanisms. This allows the model complexity to adapt to different inputs without permanently increasing the network structure.
Solution Approach 2:
The patent changes the parameters from fixed weight values to dynamically selected weights. By introducing gating networks that output weight selection probabilities and using continuous relaxation techniques, the weights transition from static parameters to dynamic parameters that change based on input conditions, thereby increasing model complexity without proportional increases in network structure.
2Measurement precision
If conventional neural networks increase model complexity to improve accuracy, then accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by using sparse weight selection through gating mechanisms. Instead of using all possible weights, the network selectively activates only the necessary weights for each input, achieving sufficient accuracy with fewer computational operations. The continuous relaxation provides just enough complexity to capture input-specific patterns without full enumeration of all weight combinations.
Solution Approach 2:
By changing weights from fixed to dynamic parameters selected via gating networks, the patent achieves higher accuracy through input-specific weight adaptation. The computational cost increases only marginally due to the efficient gating mechanism that selects weights in proportion to the number of gates, rather than exponentially with all possible weight combinations.
3Adaptability or versatility
If conditional neural network layers use continuous latent parameters to specify weights, then representational capacity increases and accuracy improves, but the network requires differentiable mappings that complicate the system
Solution Approach 1:
The patent introduces gating networks as intermediary components that bridge the input space and the weight selection space. These gating networks perform differentiable mappings from inputs to weight selection probabilities, enabling continuous relaxation and gradient-based optimization. The intermediary gates simplify the overall system by providing a structured, differentiable path from inputs to dynamic weights.
Solution Approach 2:
The patent replaces discrete weight selection mechanisms with continuous differentiable mappings. Instead of using non-differentiable operations like hard sampling or discrete switches, the patent employs softmax functions and continuous relaxation techniques that enable gradient flow through the weight selection process, facilitating end-to-end training while maintaining representational capacity.
Data Source
AI summary
Methods, systems, and apparatus for more efficiently and accurately generating neural network outputs, for instance, for use in classifying image or audio data. In one aspect, a method includes processing a network input using a neural network including multiple neural network layers to generate a network output. One or more of the neural network layers is a conditional neural network layer. Processing a layer input using a conditional neural network layer to generate a layer output includes obtaining values of one or more decision parameters of the conditional neural network layer. The neural network processes the layer input and the decision parameters of the conditional neural network layer to determine values of one or more latent parameters of the conditional neural network layer from a continuous set of possible latent parameter values. The values of the latent parameters specify the values of the conditional layer weights.