Exploiting Sparsity

Exploiting sparsity in data and models is a crucial research area aiming to improve efficiency and performance in various machine learning applications. Current efforts focus on developing techniques to handle sparse data, such as LiDAR point clouds and knowledge graphs, and to create sparse model architectures, including pruned neural networks and sparse graph transformers, through methods like graphical lasso and contrastive learning. These advancements lead to reduced computational costs, improved energy efficiency, and enhanced model interpretability, impacting fields like autonomous driving, graph-based prediction, and large-scale model training.