Omnidirectional Transformer Attention With Layerwise Meta-Learning
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Solution Overview
Problem
Existing machine learning models face challenges in efficiently leveraging omnidirectional attention mechanisms due to their computationally expensive costs and the risk of degeneration into flat networks, limiting their performance and resource utilization.
Innovation Solution
Implementing a meta-learner, such as a self-attention model, to refine intermediate representations across all layers of a transformer model, using efficient parameterization techniques like kernel-based, low-rank, and block-based sparsity to mitigate computational costs and enable omnidirectional attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If omnidirectional attention mechanisms are implemented in machine learning models, then global contextual representation learning is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the omnidirectional attention mechanism into two distinct components: a multi-layer attention model that processes intermediate representations layer-by-layer, and a separate omnidirectional model that aggregates information from all layers. This segmentation allows the system to achieve omnidirectional context without requiring every token to attend to every other token simultaneously, thereby reducing computational complexity while maintaining global contextual representation capability.
2Productivity
If model size is reduced to lower resource consumption, then efficiency is improved, but performance may deteriorate
Solution Approach 1:
The patent introduces an intermediary omnidirectional model that sits between the multi-layer attention model and the final output. This intermediary component efficiently aggregates information from all intermediate layers without requiring the full complexity of omnidirectional attention at every layer. The intermediary model uses learned aggregation weights to combine representations, achieving high performance with reduced computational overhead compared to full omnidirectional attention.
Data Source
AI summary
Provided are machine-learned attention models that feature omnidirectional processing, example implementations of which can be referred to as Omnidirectional Representations from Transformers (OMNINET). In example models described in the present disclosure, instead of maintaining a strictly horizontal receptive field, each token is allowed to attend to all tokens in some or all of the other tokens across the entire network.