Neural Network Attention Using Distance Correlation for Complex Patterns
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Solution Overview
Problem
Existing attention mechanisms in neural networks struggle with complex or non-linear input data, leading to information loss, increased training time, and computational resource usage, making them less sensitive to important data and more sensitive to unimportant data.
Innovation Solution
Implementing improved attention architectures that use distance covariance or correlation on embeddings to generate context vectors, incorporating operations like tile, repeat, broadcast, and double centering to expand input data and enhance sensitivity to complex data patterns without increasing model size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple attention components (multi-head attention) are used to handle complex or non-linear input data, then the ability to detect complex patterns is improved, but training time and computational resource usage increase
Solution Approach 1:
The patent changes the fundamental parameters of the attention mechanism by using distance covariance and distance correlation instead of traditional attention scores. This allows the system to detect complex non-linear patterns while maintaining computational efficiency, as these statistical measures can capture complex relationships without requiring multiple stacked attention heads
Solution Approach 2:
The patent segments the attention computation into distinct statistical operations: computing distance matrices, calculating distance covariance, and calculating distance correlation. This segmentation allows each operation to be optimized independently and avoids the computational redundancy of multi-head attention while preserving the ability to detect complex patterns
2Measurement precision
If multiple attention components (multi-head attention) are used to handle complex or non-linear input data, then the ability to detect complex patterns is improved, but computational resource usage increases
Solution Approach 1:
The patent changes the computational parameters from multiple attention head computations to single distance covariance and correlation computations. This parameter change reduces computational resource usage while maintaining the ability to detect complex patterns through the statistical properties of distance-based measures
Solution Approach 2:
The patent extracts the essential computational operations needed for pattern detection (distance calculations, covariance, correlation) and removes redundant operations inherent in multi-head attention. This extraction maintains detection capability while reducing computational overhead
3Ease of operation
If traditional attention mechanisms are used, then the neural network can focus on certain elements, but information loss occurs and sensitivity to important data decreases
Solution Approach 1:
The patent introduces distance covariance and distance correlation as intermediary statistical measures that preserve information during the attention computation. These intermediaries capture the relationships between input elements without discarding information, unlike traditional attention mechanisms that may lose information through softmax normalization and selective focusing
Solution Approach 2:
The patent substitutes the mechanical attention mechanism (softmax-weighted sum) with a statistical mechanics approach (distance covariance and correlation). This substitution preserves information by using statistical moments that inherently retain relationships between all data points rather than selecting only certain elements
Data Source
AI summary
The disclosure relates to systems and methods of improved attention for neural networks. A system may access a first array of entries and a second array of entries, wherein the first array represents a first word embedding generated from one or more first characters of an input and the second array represents a second word embedding from the one or more second characters of the input. A system may generate at least a first difference matrix based on the first array and at least a second difference matrix based on the second array. A system may determine a difference value based on vector based at least in part on the difference value. A system may provide the context vector to subsequent layers of a neural network.