Combined Neural Network for Smoother Sequence Prediction
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Solution Overview
Problem
Existing neural networks struggle to accurately predict sequences of values, often resulting in large spikes or dips due to misalignment of individual predictions, and lack robustness in handling variable inputs.
Innovation Solution
A combined neural network is created by combining a first neural network trained to predict individual values with a second neural network, where the weights of the first network are maintained and only the weights of the second network are updated during training, using a loss function to refine the combined model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single neural network is used to predict sequences of values, then the network can process inputs efficiently, but the prediction accuracy deteriorates due to large spikes or dips from misaligned individual predictions
Solution Approach 1:
The system segments the prediction task into two specialized neural networks: a first neural network trained to predict individual values with high precision, and a second neural network that processes sequences of these individual predictions. This segmentation allows each network to specialize in its respective function, improving overall prediction accuracy while maintaining robustness through the coordinated interaction of the two networks.
2Adaptability or versatility
If all weights in a combined neural network are updated during training, then the network can adapt to new patterns, but the training complexity and computational cost increase
Solution Approach 1:
The training process is segmented into two phases: first, the initial neural network is trained independently to predict individual values; second, only the weights of the second neural network are updated while maintaining the first network's weights unchanged. This segmentation reduces training complexity by avoiding full-network retraining while preserving adaptability through the sequential training approach.
Solution Approach 2:
The first neural network is trained in advance to predict individual values before being combined with the second neural network. This preliminary action ensures that the first network is already optimized for its specific task, allowing the subsequent training phase to focus only on the second network's weights and reducing overall training complexity.
3Measurement precision
If a combined neural network is created to predict sequences, then prediction accuracy improves, but the device complexity increases due to multiple networks and weight management
Solution Approach 1:
The system merges a first neural network (specialized in individual value prediction) with a second neural network (specialized in sequence processing) to create a combined architecture. This merging leverages the strengths of both networks: the first network provides accurate individual predictions, while the second network processes these predictions into coherent sequences, achieving high sequence prediction accuracy without excessive complexity.
Solution Approach 2:
The first neural network is pre-trained to predict individual values before being integrated with the second neural network. This preliminary training ensures that the first network is already optimized, reducing the complexity of the combined system by eliminating the need to retrain it when integrated, thus managing device complexity effectively.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for creating a combined neural network. One of the methods includes creating a combined neural network by combining a) a first neural network that includes a first plurality of weights and was trained to predict single output values that have a type with b) a second neural network that includes a second plurality of weights, the combined neural network created to predict a sequence of values, each value of which has the type; training the combined neural network by: determining a loss function for the combined neural network using a result of a comparison of training output data and expected output data; and updating one or more weights in the second plurality of weights for the second neural network using the loss function; and storing the combined neural network in memory.