Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments

6 August 2024

Papers citing "Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments"

3 / 3 papers shown

Title
Choose Your Model Size: Any Compression by a Single Gradient Descent Martin Genzel Patrick Putzky Pengfei Zhao S. Mattes Mollenhauer Robert Seidel Stefan Dietzel Thomas Wollmann 41 0 0 03 Feb 2025
AutoML to Date and Beyond: Challenges and Opportunities Shubhra (Santu) Karmaker Md. Mahadi Hassan Micah J. Smith Lei Xu Chengxiang Zhai K. Veeramachaneni 66 222 0 21 Oct 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 219 382 0 05 Mar 2020