Summary of Ptsbench: a Comprehensive Post-training Sparsity Benchmark Towards Algorithms and Models, by Zining Wnag et al.
PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models
by Zining Wnag, Jinyang Guo, Ruihao Gong, Yang Yong, Aishan Liu, Yushi Huang, Jiaheng Liu, Xianglong Liu
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper, PTSBench, aims to comprehensively investigate post-training sparsity (PTS) algorithms and models. To achieve this, the authors benchmark 10+ fine-grained techniques on 3 typical tasks using over 40 off-the-shelf model architectures. The goal is to provide valuable insights from both algorithmic and model aspects, enabling a better understanding of PTS methods and sparsification-friendly model design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new benchmark for post-training sparsity called PTSBench. This comprehensive benchmark aims to investigate the effectiveness of various algorithms and models in achieving sparse representations. The authors experiment with different techniques on multiple tasks using a variety of pre-trained models, providing valuable insights into what works best for which scenarios. |
