Gated Attention Networks for Stable and Faster Training
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Solution Overview
Problem
Conventional attention neural networks face instability during training and slow learning speeds, particularly in domains like reinforcement learning, and are not scalable for complex problems.
Innovation Solution
Implementing a gating mechanism to replace residual connections in attention blocks and reordering layer normalization operations to allow an identity map, enabling faster and more stable training, especially in reinforcement learning settings.
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 and slow learning speed occur
Solution Approach 1:
A gating mechanism is introduced as an intermediary component between the attention layer output and the final output. This gating mechanism selectively modulates the attention output based on the input sequence, acting as a mediator that stabilizes training by preventing direct propagation of unstable gradients while maintaining learning efficiency through selective information flow.
Solution Approach 2:
The patent modifies the network architecture by changing the parameter configuration to include gating parameters that control the flow of information. By introducing learnable gating parameters that interact with the attention output, the system achieves more stable training dynamics and faster convergence compared to conventional attention networks without such parameter modifications.
2Adaptability or versatility
If larger and deeper networks are implemented, then complex tasks can be handled, but computational resources increase
Solution Approach 1:
The gating mechanism introduces dynamic control over information flow in the network. By making the gating parameters learnable and adaptive, the network can dynamically adjust the amount of information processed at each layer, allowing larger and deeper networks to be more efficient. The gating mechanism enables the network to adapt its computational complexity based on the specific task requirements, reducing unnecessary computational resources while maintaining the capability to handle complex tasks.
Data Source
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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.