Time-based attention

Time-based attention is a method used in machine learning models, especially in analyzing sequential data like speech or text, where the model focuses more on specific moments or periods in the sequence. It assigns different importance levels to different time points, helping the model prioritize relevant information. For example, in language translation, it might emphasize certain words over others based on their significance in context. This approach allows the model to efficiently process complex sequences by selectively concentrating on important segments, improving accuracy and understanding without processing every part with equal weight.