Self-attention is a method that enables each element in a sequence to focus on all other elements, effectively capturing dependencies by calculating weighted representations based on their similarities.
Multi-head attention expands on this concept by employing multiple parallel attention mechanisms, or "heads," to learn varied representations of the input. Each head processes the sequence independently, and their outputs are combined, which enhances the model's expressiveness and its ability to capture complex relationships.
For a more in-depth understanding of these algorithms and their distinctions, you can check out the following research paper: https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html.
In MATLAB, you can implement both self-attention and multi-head attention algorithms using the "attention" function, which is included in the "Deep Learning Toolbox." For further details on the "attention" function, visit this documentation page: https://www.mathworks.com/help/deeplearning/ref/dlarray.attention.html.
To create a self-attention mechanism, simply use the "attention" function by providing the same vector for the "queries," "keys," and "values" arguments, and set the "numHeads" argument to 1. If you're looking to create a multi-head attention mechanism, you can refer to the example provided here: https://www.mathworks.com/help/deeplearning/ref/dlarray.attention.html#mw_b91954aa-57f1-4991-bfa2-3dffde48e181.
For additional information on the algorithms used in MATLAB to achieve these functionalities, you can consult the documentation at https://www.mathworks.com/help/deeplearning/ref/dlarray.attention.html#mw_ea5a9cc6-dc63-428d-a780-20ed516b4927.
I hope this gives you a clearer understanding of the self-attention and multi-head attention mechanisms and how to implement them in MATLAB.
