Gated Attention Neural Networks for Stable, Efficient Training
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Solution Overview
Problem
Conventional attention neural networks face instability during training, leading to slower learning speeds and suboptimal performance, especially in domains like reinforcement learning, and are not scalable for complex problems.
Innovation Solution
Replace residual connections in attention blocks with gating functions and reorder layer normalization operations to stabilize training and improve learning efficiency, allowing for faster and more reliable training of attention neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional attention neural networks are used, then the basic attention mechanism can be implemented, but training instability occurs leading to slower learning speeds and suboptimal performance
Solution Approach 1:
A gating mechanism is introduced as an intermediary component between the attention mechanism and the output. This gate controls the flow of information by multiplying the attention output with a gating signal, thereby stabilizing training by preventing direct propagation of unstable gradients while maintaining learning capability through controlled information flow.
Solution Approach 2:
The patent modifies the training parameters and architecture by introducing gating parameters that can be learned during training. These parameters allow dynamic adjustment of the gating mechanism's influence, enabling the network to adapt to different training stages and optimize both stability and learning speed through parameter optimization.
2Adaptability or versatility
If conventional attention neural networks are used, then basic processing can be achieved, but they are not scalable for complex problems
Solution Approach 1:
The network is divided into modular blocks, each containing an attention mechanism combined with a gating mechanism. This segmentation allows the network to handle complex problems by stacking multiple identical or variation modules, where each module processes a subset of the complexity independently, thereby achieving scalability without proportionally increasing overall system complexity.
Solution Approach 2:
The gating mechanism serves multiple functions simultaneously: it stabilizes training, controls information flow, and adapts to different problem complexities. This multi-functionality allows a single architectural component to address various challenges across different problem domains, enhancing scalability without requiring separate specialized mechanisms for each function.
3Productivity
If training is performed with conventional attention networks, then learning can occur, but computational resources are excessive and efficiency is reduced
Solution Approach 1:
The gating mechanism extracts and selectively transmits only the most relevant information from the attention output to the subsequent layers. By multiplying the attention output with a gating signal that highlights important features and suppresses less relevant ones, the network reduces computational waste by focusing resources on processing only the essential information, thereby improving training efficiency and reducing energy consumption.
Data Source
AI summary
A system including an attention neural network that is configured to receive an input sequence and to process the input sequence to generate an output is described. The attention neural network includes: an attention block configured to receive a query input, a key input, and a value input that are derived from an attention block input. The attention block includes an attention neural network layer configured to: receive an attention layer input derived from the query input, the key input, and the value input, and apply an attention mechanism to the query input, the key input, and the value input to generate an attention layer output for the attention neural network layer; and a gating neural network layer configured to apply a gating mechanism to the attention block input and the attention layer output of the attention neural network layer to generate a gated attention output.