Compression Task

Compression tasks in machine learning aim to reduce the size and computational cost of models while preserving performance. Current research focuses on developing efficient compression frameworks for various model architectures, including transformers and state-space models, often employing techniques like knowledge distillation, pruning, and reduced-order modeling. These advancements are crucial for deploying complex models on resource-constrained devices and improving the efficiency of large-scale applications such as image and video processing, natural language processing, and federated learning. The ultimate goal is to achieve a favorable trade-off between model size, computational speed, and accuracy.