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AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters.

, , and . NeurIPS, page 13681-13691. (2019)

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Efficient Randomized Feature Selection Algorithms., and . HPCC/SmartCity/DSS, page 796-803. IEEE, (2019)AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters., , and . NeurIPS, page 13681-13691. (2019)Towards Lossless Head Pruning through Automatic Peer Distillation for Language Models., , , , , and . IJCAI, page 5113-5121. ijcai.org, (2023)Against Membership Inference Attack: Pruning is All You Need., , , , , , , and . IJCAI, page 3141-3147. ijcai.org, (2021)Novel and efficient randomized algorithms for feature selection., , and . Big Data Min. Anal., 3 (3): 208-224 (2020)Efficient Sequential and Parallel Algorithms for Estimating Higher Order Spectra., , , , and . CIKM, page 1743-1752. ACM, (2019)MCMIA: Model Compression Against Membership Inference Attack in Deep Neural Networks., , , , , , , and . CoRR, (2020)HMSC: a Hybrid Metagenomic Sequence Classification Algorithm., , and . BCB, page 19:1-19:6. ACM, (2020)Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic., , , and . CoRR, (2022)AutoReP: Automatic ReLU Replacement for Fast Private Network Inference., , , , , , , , , and 4 other author(s). ICCV, page 5155-5165. IEEE, (2023)