2019/12/10 by Vinnie Ko, Ko, Vinnie, Stefan Oehmcke +3
Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Algorithm #Anomaly Detection Techniques and Applications #Artificial intelligence #Artificial neural network #Benchmark (surveying) #Computer science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Machine learning #Magnitude (astronomy) #Overfitting #Pruning #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1912.04845
published in arXiv (Cornell University) (Cornell University) · 10 pages
arxiv created 2019/12/10 · openalex publication_date 2019/12/10 · arxiv updated 2019/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Neural networks have achieved dramatic improvements in recent years and depict the state-of-the-art methods for many real-world tasks nowadays. One drawback is, however, that many of these models are overparameterized, which makes them both computationally and memory intensive. Furthermore, overparameterization can also lead to undesired overfitting side-effects. Inspired by recently proposed magnitude-based pruning schemes and the Wald test from the field of statistics, we introduce a novel magnitude and uncertainty (M&U) pruning criterion that helps to lessen such shortcomings. One important advantage of our M&U pruning criterion is that it is scale-invariant, a phenomenon that the magnitude-based pruning criterion suffers from. In addition, we present a ``pseudo bootstrap'' scheme, which can efficiently estimate the uncertainty of the weights by using their update information during training. Our experimental evaluation, which is based on various neural network architectures and datasets, shows that our new criterion leads to more compressed models compared to models that are solely based on magnitude-based pruning criteria, with, at the same time, less loss in predictive power.