Dynamic Pruning

Dynamic pruning is a technique for improving the efficiency of neural networks by selectively removing less important parameters or computations during training or inference. Current research focuses on developing efficient algorithms for dynamic pruning in various architectures, including Vision Transformers (ViTs), Graph Neural Networks (GNNs), and large language models, often incorporating techniques like self-attention and meta-learning to guide the pruning process and minimize accuracy loss. These advancements aim to reduce computational costs and memory requirements, making large-scale deep learning models more deployable on resource-constrained devices and accelerating training times for various applications, such as recommendation systems and computer vision. The ultimate goal is to achieve significant computational savings without sacrificing model performance.