Artificial Intelligence, Deep Learning, Machine Learning, Neural Networks, Math.
the model’s computational resources, reducing the overall complexity from O(n²) to O(n log n). 2. Low-Rank Attention Theoretical Insight: Low-rank factorization posits that the interaction space can be effectively captured by a smaller subspace, reducing the need for full n×n attention computation.