A mechanism in neural networks that computes weighted relevance scores between elements of a sequence, allowing the model to focus on the most pertinent info...
The way AI decides which words in a sentence are most important โ like when you highlight the key words in a book.
A technique that lets AI focus on the most relevant parts of the input when generating each word, instead of treating everything equally.
A mechanism in neural networks that computes weighted relevance scores between elements of a sequence, allowing the model to focus on the most pertinent information for each output.
The core operation in transformers that computes pairwise relevance via scaled dot-product of query, key, and value projections, enabling dynamic context-dependent weighting of input representations.
Scaled dot-product attention: Attention(Q,K,V) = softmax(QKแต/โdโ)V โ extended via multi-head projections, causal masking, and positional encodings, with O(nยฒ) complexity driving research into linear, sparse, and sub-quadratic alternatives.
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