Augmented Neural Network Sequence Processing for Output Accuracy
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Solution Overview
Problem
Neural networks face challenges in determining when a final output has been generated for a given input, leading to potential inaccuracies and inefficiencies, particularly for complex inputs, as they may produce incomplete or unnecessary additional outputs.
Innovation Solution
An augmented neural network system that includes a sequence processing subsystem to determine when a final output has been generated, allowing for more accurate and efficient processing by deciding whether to produce additional outputs based on designated portions of the neural network output, and utilizing an external memory for enhanced performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the neural network generates multiple additional outputs for complex inputs, then the accuracy of the final output is improved, but the processing time and computing resources increase
Solution Approach 1:
The system dynamically adjusts the number of processing passes based on input complexity. The sequence processing subsystem monitors intermediate outputs and determines whether to generate additional outputs or terminate processing early, making the processing depth adaptive rather than fixed. This resolves the contradiction by allowing multiple passes only when necessary for complex inputs while maintaining single-pass efficiency for simpler cases.
Solution Approach 2:
The system changes the parameter of output generation from a fixed multiple outputs approach to a conditional single or multiple outputs approach. By using a designated portion of the neural network output to control whether additional outputs are generated, the system optimizes the balance between accuracy and processing time based on the specific input characteristics.
2Reliability
If the neural network generates additional outputs for all inputs, then the reliability of the final output is improved, but the computing resources required increase
Solution Approach 1:
The system applies different processing strategies to different inputs based on their characteristics. By examining a designated portion of the neural network output and making localized decisions about whether additional outputs are needed, the system ensures high reliability only where necessary rather than uniformly applying additional processing to all inputs, thus reducing overall computing resource requirements.
Solution Approach 2:
The system performs partial action by generating additional outputs only when the designated portion of the neural network output indicates it is necessary. This avoids the excessive action of generating multiple outputs for all inputs, thereby maintaining reliability when needed while reducing computing resource consumption for cases where full processing is not required.
3Measurement precision
If the system waits for complete outputs before further processing, then the accuracy is improved, but the productivity decreases
Solution Approach 1:
The system performs preliminary action by generating an initial output early in the processing sequence. This initial output can be used for preliminary processing or intermediate purposes while the decision about generating additional outputs is being made. This resolves the contradiction by allowing some processing to proceed in parallel rather than waiting for complete outputs, thus maintaining productivity while still ensuring accuracy when needed.
Solution Approach 2:
The system dynamically determines the processing sequence based on input characteristics. By using the designated portion of the neural network output to control whether additional outputs are generated and when processing should terminate, the system optimizes the balance between output completeness and processing throughput, allowing faster processing when complete outputs are not strictly necessary.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks to generate additional outputs. One of the systems includes a neural network and a sequence processing subsystem, wherein the sequence processing subsystem is configured to perform operations comprising, for each of the system inputs in a sequence of system inputs: receiving the system input; generating an initial neural network input from the system input; causing the neural network to process the initial neural network input to generate an initial neural network output for the system input; and determining, from a first portion of the initial neural network output for the system input, whether or not to cause the neural network to generate one or more additional neural network outputs for the system input.