Adaptive Pruning

Adaptive pruning techniques aim to improve the efficiency and performance of machine learning models by selectively removing less important parameters or components. Current research focuses on developing algorithms that dynamically adjust pruning strategies based on model performance and data characteristics, applied to various architectures including random forests, vision transformers, and deep neural networks. This work is significant because it addresses the computational cost and resource limitations associated with large models, enabling faster training and inference, particularly beneficial for resource-constrained applications like federated learning and deployment on edge devices. The resulting smaller, faster models also enhance interpretability and reduce energy consumption.