Convolution-Enhanced Evolving Attention Networks.

Area of Science:

• Artificial Intelligence
• Deep Learning
• Computer Vision
• Natural Language Processing
• Time-Series Analysis

Background:

• Attention networks, like Transformers, are vital in AI, with attention maps encoding semantic dependencies.
• Current models learn attention maps independently per layer, lacking explicit inter-layer knowledge transfer.
• This limits the ability to capture evolving inter-token relationships across different abstraction levels.

Purpose of the Study:

• To propose a novel evolving attention mechanism that directly models the evolution of inter-token relationships.
• To enhance information flow and knowledge transfer between attention map layers.
• To improve the performance of attention-based neural networks across diverse AI applications.

Main Methods:

• Introduced a generic evolving attention mechanism utilizing residual convolutional modules.
• Modeled the layer-wise evolution of attention maps explicitly, unlike previous separate learning approaches.
• Integrated this mechanism into convolution-enhanced evolving attention networks.

Main Results:

• Achieved superior performance in time-series representation, natural language understanding, machine translation, and image classification.
• The Evolving Attention-enhanced Dilated Convolutional (EA-DC-) Transformer showed significant gains, with an average 17% improvement on time-series tasks.
• Demonstrated the effectiveness of modeling attention map evolution for enhanced AI model capabilities.

Conclusions:

• The proposed evolving attention mechanism offers a new paradigm for attention-based neural networks.
• Explicitly modeling the layer-wise evolution of attention maps enhances model performance across various domains.
• This work pioneers the explicit modeling of attention map evolution, setting a new benchmark in AI research.