2022/02/24 by Kartik Sreenivasan, Kartik K. Sreenivasan, Jy-yong Sohn +17 · 4 citations
Computer Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Stochastic Gradient Optimization Techniques #cs.AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2202.12002
openalex publication_date 2022/02/24 · arxiv created 2022/06/02 · arxiv updated 2022/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming "train, prune, re-train" approach. Frankle & Carbin conjecture that we can avoid this by training "lottery tickets", i.e., special sparse subnetworks found at initialization, that can be trained to high accuracy. However, a subsequent line of work by Frankle et al. and Su et al. presents concrete evidence that current algorithms for finding trainable networks at initialization, fail simple baseline comparisons, e.g., against training random sparse subnetworks. Finding lottery tickets that train to better accuracy compared to simple baselines remains an open problem. In this work, we resolve this open problem by proposing Gem-Miner which finds lottery tickets at initialization that beat current baselines. Gem-Miner finds lottery tickets trainable to accuracy competitive or better than Iterative Magnitude Pruning (IMP), and does so up to 19× faster.