Define the dot convention in transformers.
1 Answer
To provide you with the most accurate information, I can explain some common concepts related to transformers:
Transformer: A transformer is a deep learning architecture introduced in the paper "Attention Is All You Need" by Vaswani et al. (2017). It revolutionized various natural language processing tasks by using self-attention mechanisms instead of recurrent networks like LSTM or GRU.
Self-Attention: Self-attention, also known as intra-attention or scaled dot-product attention, is a mechanism that allows the model to weigh the importance of different positions (or tokens) in the input sequence when processing each position. It calculates attention scores between all pairs of input positions and uses them to compute a weighted sum of the values at those positions.
Attention Matrix: The attention matrix is the result of the self-attention mechanism and represents the importance weights given to different positions in the input sequence when processing a specific position.
Dot-Product Attention: In dot-product attention, the attention scores are computed by taking the dot product of a query vector with a set of key vectors and then applying a softmax function to obtain the attention weights. This is one of the key components used in the transformer model.
Transformer: A transformer is a deep learning architecture introduced in the paper "Attention Is All You Need" by Vaswani et al. (2017). It revolutionized various natural language processing tasks by using self-attention mechanisms instead of recurrent networks like LSTM or GRU.
Self-Attention: Self-attention, also known as intra-attention or scaled dot-product attention, is a mechanism that allows the model to weigh the importance of different positions (or tokens) in the input sequence when processing each position. It calculates attention scores between all pairs of input positions and uses them to compute a weighted sum of the values at those positions.
Attention Matrix: The attention matrix is the result of the self-attention mechanism and represents the importance weights given to different positions in the input sequence when processing a specific position.
Dot-Product Attention: In dot-product attention, the attention scores are computed by taking the dot product of a query vector with a set of key vectors and then applying a softmax function to obtain the attention weights. This is one of the key components used in the transformer model.