👉 Pads computing, also known as attention-based computation or transformer-based computation, is a mechanism used in deep learning models, particularly those based on the Transformer architecture, to weigh the importance of different input elements when processing or generating output. In this framework, each element in an input sequence (like words in a sentence) is associated with a set of learned weights that determine how much influence that element should have on the computation for subsequent steps. These weights are dynamically computed during training using self-attention mechanisms, which allow the model to focus on relevant parts of the input while generating each part of the output. This process enables the model to capture complex dependencies and relationships within the data, making it highly effective for tasks like machine translation, text summarization, and more. By efficiently managing the interactions between different parts of the input, pads computing facilitates the model's ability to generate coherent and contextually relevant outputs.
