Deepmind technologies limited (20240320469). GATED ATTENTION NEURAL NETWORKS simplified abstract

GATED ATTENTION NEURAL NETWORKS

Organization Name

deepmind technologies limited

Inventor(s)

Emilio Parisotto of London (GB)

Hasuk Song of London (GB)

Jack William Rae of London (GB)

Siddhant Madhu Jayakumar of London (GB)

Maxwell Elliot Jaderberg of London (GB)

Razvan Pascanu of Letchworth Garden City (GB)

Caglar Gulcehre of Lausanne (CH)

GATED ATTENTION NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240320469 titled 'GATED ATTENTION NEURAL NETWORKS

The abstract describes a system with an attention neural network that processes input sequences to generate outputs. The attention neural network includes an attention block with an attention neural network layer and a gating neural network layer.

• The system includes an attention neural network that processes input sequences.
• The attention block receives query, key, and value inputs to generate an attention layer output.
• The attention neural network layer applies an attention mechanism to the inputs to generate the attention layer output.
• The gating neural network layer applies a gating mechanism to the attention block input and the attention layer output to generate a gated attention output.

Potential Applications: - Natural language processing - Machine translation - Image recognition

Problems Solved: - Enhancing the accuracy of sequence processing - Improving the performance of neural networks

Benefits: - Increased efficiency in processing input sequences - Enhanced performance in various applications

Commercial Applications: - AI-powered chatbots - Automated language translation services - Image and video analysis tools

Questions about Attention Neural Network: 1. How does the attention mechanism improve the processing of input sequences? 2. What are the key advantages of using a gating mechanism in the attention neural network?

Frequently Updated Research: - Stay updated on advancements in attention mechanisms in neural networks for improved performance.

Original Abstract Submitted

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.