CRESPR: Modular sparsification of DNNs to improve pruning performance and model interpretability

Author:

Kang Tianyu,Ding Wei,Chen PingORCID

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference59 articles.

1. The generalization-stability tradeoff in neural network pruning;Bartoldson,2020

2. Gradient and mangitude based pruning for sparse deep neural networks;Belay;Proceedings of the AAAI Conference on Artificial Intelligence,2022

3. Bellec, G., Kappel, D., Maass, W., & Legenstein, R. (2018). Deep rewiring: Training very sparse deep networks. In ICLR, 2018.

4. Bibikar, S., Vikalo, H., Wang, Z., & Chen, X. (2022). Federated dynamic sparse training: Computing less, communicating less, yet learning better. In Proceedings of the AAAI conference on artificial intelligence, vol. 36, no. 6 (pp. 6080–6088).

5. Language models are few-shot learners;Brown,2020

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