Explore the lottery ticket hypothesis for large pre-trained models in this seminar presented by Professor Atlas Wang from UT Austin. Delve into the fascinating world of machine learning, computer vision, and optimization as Wang discusses his research on finding smaller, trainable subnetworks within enormous pre-trained models. Learn about the application of this hypothesis to both NLP and computer vision domains, with specific examples from BERT and ImageNet pre-trained models. Discover how these subnetworks can achieve high levels of sparsity while maintaining full accuracy and transferability to downstream tasks. Gain insights into the implications of this research for the future of deep learning and large-scale pre-training paradigms. Access additional resources and project details through the provided webpage link.