Neural attention scaling refers to the methods and techniques used to make attention mechanisms in neural networks work efficiently with very large datasets or models. As models grow in size and complexity, calculating attention for every part of the data can become extremely demanding. Scaling solutions aim to reduce the computational resources needed, either by…

Neural compression algorithms use artificial neural networks to reduce the size of digital data such as images, audio, or video. They learn to find patterns and redundancies in the data, allowing them to represent the original content with fewer bits while keeping quality as high as possible. These algorithms are often more efficient than traditional…

Neural pruning strategies refer to methods used to remove unnecessary or less important parts of a neural network, such as certain connections or neurons. The goal is to make the network smaller and faster without significantly reducing its accuracy. This helps in saving computational resources and can make it easier to run models on devices…