Merger Neural Network Blocks Sequence Compression
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Solution Overview
Problem
Existing neural networks face inefficiencies in processing input sequences due to the computational resources required to handle large numbers of input elements, which can lead to increased runtime and computational costs.
Innovation Solution
The integration of merger neural network blocks into the neural network architecture, which reduces the number of elements in the input sequence by 'merging' them into a smaller set of output elements, thereby improving computational efficiency and reducing the resources needed for subsequent processing layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the neural network processes all input elements through subsequent layers, then the processing is complete and accurate, but the computational cost and runtime increase significantly
Solution Approach 1:
The patent divides the input sequence into multiple segments or groups, processing them through different pathways. Some segments are processed fully while others are merged earlier, creating a segmented processing architecture that balances accuracy and efficiency.
Solution Approach 2:
The patent introduces merger blocks that combine multiple input elements into fewer representation vectors at strategic points in the network. This merging reduces the number of elements subsequent layers must process while preserving essential information through learned compression transformations.
2Productivity
If the neural network reduces the number of elements early in processing, then computational efficiency improves, but information loss may occur
Solution Approach 1:
The patent transforms input elements into different parameter spaces through learned linear transformations. By changing the parameter representation (from raw input vectors to transformed representation vectors), the network compresses information efficiently while maintaining the essential characteristics needed for accurate prediction.
3Reliability
If more neural network layers are added to improve accuracy, then prediction quality increases, but the computational resources and time required increase
Solution Approach 1:
By introducing merger blocks that reduce sequence length at intermediate stages, the patent enables the network to accommodate more layers within the same computational budget. The merged representations require fewer operations in subsequent layers, allowing deeper architectures without proportionally increasing runtime.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more merger neural network blocks that each generate block output sequence that has fewer elements than the block input sequence that is processed by the merger neural network block.