Sparse Pattern

Sparse pattern research focuses on efficiently representing and utilizing sparse data structures within large-scale models, primarily aiming to reduce computational complexity and memory footprint without significant performance loss. Current efforts concentrate on developing adaptive and structured sparsity patterns for transformer networks and convolutional neural networks, employing techniques like dynamically generated masks, submodular optimization, and convolutional flood filling to achieve optimal sparsity. This work is crucial for deploying large models on resource-constrained devices and improving the training efficiency of computationally expensive architectures, impacting both fundamental machine learning research and practical applications in natural language processing and computer vision.