Sparsifying Graph Neural Networks with Compressive Sensing
Author:
Affiliation:
1. Electrical and Computer Engineering, Wayne State University, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3649476.3658780
Reference15 articles.
1. Stochastically Estimating Modular Criticality in Large-Scale Logic Circuits Using Sparsity Regularization and Compressive Sensing
2. MAGNNETO: A Graph Neural Network-Based Multi-Agent System for Traffic Engineering
3. Tri Dao, Beidi Chen, Kaizhao Liang, Jiaming Yang, Zhao Song, Atri Rudra, and Christopher Re. 2022. Pixelated butterfly: Simple and efficient sparse training for neural network models. In Proceedings of the International Conference on Learning Representations (ICLR).
4. Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation
5. Stochastically Pruning Large Language Models Using Sparsity Regularization and Compressive Sensing
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