2023/03/26 by Eirik Fladmark, Fladmark, Eirik, Muhammad Hamza Sajjad +3 · 1 citation
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications #Sports Analytics and Performance
paper · pdf · doi:10.48550/arxiv.2303.15479
openalex publication_date 2023/03/26 · openalex created_date 2023/03/31 · openalex updated_date 2026/07/28
In this paper, we explore the performance of different pruning methods in the context of the lottery ticket hypothesis. We compare the performance of L1 unstructured pruning, Fisher pruning, and random pruning on different network architectures and pruning scenarios. The experiments include an evaluation of one-shot and iterative pruning, an examination of weight movement in the network during pruning, a comparison of the pruning methods on networks of varying widths, and an analysis of the performance of the methods when the network becomes very sparse. Additionally, we propose and evaluate a new method for efficient computation of Fisher pruning, known as batched Fisher pruning.