Researchers have developed a new approach to approximating self-attention, a key component of transformer models, using the Nyström method. This allows for linear time and memory complexity, making it more efficient than traditional methods. The Nyströmformer uses a random subset of input embeddings to approximate the full attention matrix, reducing computational requirements without sacrificing accuracy. --- Why it matters: This matters because self-attention is a computationally expensive operation in transformer models, limiting their scalability. By approximating it efficiently, researchers can build larger and more complex models for tasks like natural language processing and computer vision. Source: https://huggingface.co/blog/nystromformer