Lottery Ticket

The "lottery ticket hypothesis" posits that large, overparameterized neural networks contain smaller, highly accurate subnetworks ("winning tickets") that can be identified through pruning techniques and trained independently. Current research focuses on improving the efficiency and effectiveness of finding these winning tickets, exploring various pruning algorithms (e.g., iterative magnitude pruning) and applying them to diverse architectures, including convolutional neural networks, transformers, and graph neural networks. This research aims to reduce the computational cost and memory requirements of deep learning models, leading to more efficient training and deployment, particularly beneficial for resource-constrained applications and large-scale models.