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Neural network mechanism for relating different positions in a sequence
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Self-attention is a mechanism in neural networks where each element in a sequence computes attention weights over all other elements in the same sequence, allowing the model to capture dependencies regardless of distance. It forms the core of the transformer architecture introduced in the 2017 paper "Attention Is All You Need." Unlike recurrent approaches, self-attention processes all positions in parallel.
Self-attention is a mechanism in neural networks where each element in a sequence computes attention weights over all other elements in the same sequence, allowing the model to capture dependencies regardless of distance. It forms the core of the transformer architecture introduced in the 2017 paper "Attention Is All You Need." Unlike recurrent approaches, self-attention processes all positions in parallel.
