There are a lot of explanations elsewhere, here I’d like to share some example questions in an interview setting.
Can you explain how does the attention mechanism work for Time Series tasks?
Here are some tips for readers’ reference:
The Attention mechanism, originally introduced in the field of natural language processing (NLP), has been successfully adapted and applied to various other domains, including time series tasks. The Attention mechanism allows a model to focus on specific parts of the input sequence that are relevant to making predictions, rather than relying on a fixed-length representation or considering the entire sequence at once.
In the context of time series tasks, such as forecasting or sequence classification, the Attention mechanism can capture temporal dependencies and assign varying weights to different time steps based on their importance.
The attention mechanism works by first creating a representation of each time step in the input sequence. These representations are then used to calculate a weight for each time step. The weights are then used to create a weighted sum of the representations, which is called the context vector. The context vector is then used by the model to make predictions about the future.
In this way, Attention mechanism assigns different importance to the different elements of the input sequence, and gives more attention to the more relevant inputs. This explains the name of the model).
The following video gives a really good explanation on this. Check it out!
Check the explanation by Rasa!
Note: There are different angles to answer an interview question. The author of this newsletter does not try to find a reference that answers a question exhaustively. Rather, the author would like to share some quick insights and help the readers to think, practice and do further research as necessary.
Source of answer: Video. Rasa Algorithm Whiteboard — Transformers & Attention 1: Self Attention
Good reads: Medium. Time Series Forecasting with Deep Learning and Attention Mechanism Medium. Illustrated: Self-Attention
MLnotes post. Global attention and transformer
MLnotes post. Attention and self-attention