Many Sparse

Many Sparse research focuses on developing efficient methods for handling sparse data and models, primarily aiming to reduce computational costs and memory consumption while maintaining or improving performance. Current efforts concentrate on sparse neural network architectures (including Mixture-of-Experts models and various pruning techniques), sparse attention mechanisms in transformers, and sparse representations for various data types (e.g., point clouds, images). This work is significant for advancing machine learning applications in resource-constrained environments and enabling the scaling of large models to previously intractable sizes and complexities.