Lottery Ticket Hypothesis
The Lottery Ticket Hypothesis (LTH) posits that large, randomly initialized neural networks contain small, sparse subnetworks ("winning tickets") capable of achieving comparable or even superior performance when trained in isolation. Current research focuses on improving the efficiency and scalability of finding these winning tickets across various architectures, including convolutional neural networks (CNNs), transformers, graph neural networks (GNNs), and even diffusion models, employing techniques like iterative magnitude pruning and novel algorithms tailored to specific model types. This research is significant because it offers the potential for creating smaller, faster, and more energy-efficient deep learning models, impacting both the theoretical understanding of neural network training and practical applications requiring resource-constrained deployments.