Collectively explore the concept of sparse attention mechanisms in deep learning, primarily within the context of Transformer models. They explain how standard attention's quadratic computational and memory cost (O(n²)) limits handling long sequences and how sparse attention addresses this by only computing a subset of interactions.

Various sparse patterns, such as local window, global, random, and hybrid, are discussed, along with specific models like Longformer, Reformer, and BigBird, which implement these techniques.

The texts highlight the significant efficiency gains, enabling longer context windows for tasks in NLP, computer vision, speech recognition, and other domains, while also analyzing the critical trade-off between sparsity and model accuracy and outlining future research directions including learned sparsity and hardware-aware design.