Sparse Mask

Sparse mask techniques aim to improve the efficiency and performance of neural networks by selectively activating or updating only a subset of parameters. Current research focuses on developing efficient algorithms for creating and applying these masks, particularly within transformer architectures and diffusion models, exploring various sparsity patterns (e.g., unstructured, structured, block-sparse) and incorporating them into training processes like preference optimization and federated learning. This work is significant because it addresses the computational cost and memory limitations of large models, leading to faster training, reduced inference times, and improved resource utilization in diverse applications.