Attention | Algorithm Architecture
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Attention models are NN that can be used to focus on specific parts of an input sequence. This makes them a powerful tool for a variety of tasks, such as machine translation, text summarization, and question answering. Attention models work by first encoding the input sequence into a sequence of hidden states. These hidden states are then used to compute a weighted sum of the input sequence, where the weights are determined by the attention mechanism. The weighted sum is then used to generate the output sequence. The attention mechanism is a function that computes the weights for each hidden state. The attention mechanism can be implemented in a variety of ways, but the most common approach is to use a neural network. The neural network takes the hidden states as input and outputs a weight for each hidden state. The weights are then used to compute a weighted sum of the hidden states. The weighted sum is then used to generate the output sequence. Attention models have been shown to be effective at a variety of tasks. They are particularly effective at tasks where it is important to focus on specific parts of the input sequence. For example, attention models have been shown to be effective at machine translation, text summarization, and question answering. Here is a more detailed description of the attention model architecture: Input sequence: The input to the attention model is a sequence of tokens, such as words or characters. Encoder: The encoder is a neural network that takes the input sequence as input and outputs a sequence of hidden states. The hidden states are a representation of the input sequence. Attention mechanism: The attention mechanism is a function that computes the weights for each hidden state. The attention mechanism can be implemented in a variety of ways, but the most common approach is to use a neural network. The neural network takes the hidden states as input and outputs a weight for each hidden state. Weighted sum: The weighted sum is computed by multiplying each hidden state by its weight and then summing the products. The weighted sum is a representation of the input sequence that is focused on the important parts of the sequence. Decoder: The decoder is a neural network that takes the weighted sum as input and outputs the output sequence. The output sequence is a translation, summary, or answer to the input sequence. |