Attention Networks With Compressed Memory for Long-Range Context
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Solution Overview
Problem
Conventional neural networks face challenges in capturing long-term dependencies within sequences due to high computational and storage costs associated with maintaining extensive memory data, and the limitation of discarding older representations once memory capacity is reached.
Innovation Solution
Implementing a neural network with episodic and compressed memory systems that store and compress hidden states, allowing the network to retain past context with minimal computational overhead by using techniques like max pooling, mean pooling, or convolution functions to reduce the memory footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional neural networks maintain extensive memory data to capture long-term dependencies, then the ability to consider past contexts is improved, but computational and storage costs increase significantly
Solution Approach 1:
The patent extracts and stores only the most relevant past hidden states in episodic memory rather than maintaining all historical data. The attention mechanism selectively attends to specific past positions based on relevance, extracting only necessary information for long-term dependency capture while discarding redundant data to reduce computational burden.
Solution Approach 2:
The memory system is segmented into episodic memory (for recent past) and compressed memory (for distant past), with different retention policies. This segmentation allows the system to manage long-term dependencies by dividing the memory burden into manageable segments with different levels of detail and computational requirements.
2Loss of information
If conventional neural networks maintain extensive memory data to capture long-term dependencies, then the ability to consider past contexts is improved, but storage costs increase significantly
Solution Approach 1:
The system extracts and stores only essential past hidden states in episodic memory rather than retaining all historical data. The attention mechanism identifies and stores only the most relevant past positions, significantly reducing the quantity of memory data needed while preserving long-term dependency information.
Solution Approach 2:
The patent implements a nested memory structure where compressed memory stores aggregated representations of distant past data, and episodic memory stores detailed recent past data. This nesting allows the system to maintain long-term context with minimal storage by placing detailed information only where necessary.
3Quantity of substance
If neural networks discard older representations once memory capacity is reached, then storage requirements are managed, but long-term dependency information is lost
Solution Approach 1:
The memory system is divided into episodic memory for recent past and compressed memory for distant past. This segmentation allows the system to manage memory capacity by storing detailed recent data in episodic memory while maintaining compressed representations of distant data in compressed memory, preventing information loss even when episodic memory is full.
Solution Approach 2:
The system performs preliminary compression of distant past hidden states before storing them in compressed memory. This preliminary action reduces the data volume early in the process, allowing the system to retain long-term context without exceeding memory capacity limits.
4Loss of information
If attention mechanisms process all past hidden states, then long-range sequence reasoning is improved, but computational overhead increases
Solution Approach 1:
The attention mechanism applies different quality levels to different past positions. Recent past positions in episodic memory receive full attention processing, while distant past positions in compressed memory receive reduced attention processing. This local quality differentiation maintains long-range reasoning capability while significantly reducing overall computational overhead.
Solution Approach 2:
The system applies partial attention to compressed memory representations rather than processing all past hidden states equally. By attending to compressed representations of distant past data, the system achieves sufficient long-range context without the excessive computational cost of processing every individual past state in detail.
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 that is a sequence to generate a network output. In one aspect, one of the methods includes, for each particular sequence of layer inputs: for each attention layer in the neural network: maintaining episodic memory data; maintaining compressed memory data; receiving a layer input to be processed by the attention layer; and applying an attention mechanism over (i) the compressed representation in the compressed memory data for the layer, (ii) the hidden states in the episodic memory data for the layer, and (iii) the respective hidden state at each of the plurality of input positions in the particular network input to generate a respective activation for each input position in the layer input.