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Transformers, introduced in the groundbreaking paper Attention Is All You Need , have revolutionized artificial intelligence, particularly in natural language processing and image classification. At the core of this success is the attention mechanism, which enables models to dynamically focus on relevant parts of the input. However, as transformers grow larger and deeper, the attention mechanism faces significant computational bottlenecks, especially with long input sequences.
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