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Exploring the Effect of ℓ0/ℓ2 Regularization in Neural Network Pruning using the LC Toolkit., and . ICASSP, page 3373-3377. IEEE, (2022)Low-rank Compression of Neural Networks: LC Algorithms and Open-source Implementation.. University of California, Merced, USA, (2021)Structured Multi-Hashing for Model Compression., , , , , , and . CoRR, (2019)Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer., and . CVPR, page 8046-8056. Computer Vision Foundation / IEEE, (2020)"Learning-Compression" Algorithms for Neural Net Pruning., and . CVPR, page 8532-8541. Computer Vision Foundation / IEEE Computer Society, (2018)An Empirical Comparison of Quantization, Pruning and Low-rank Neural Network Compression using the LC Toolkit., and . IJCNN, page 1-8. IEEE, (2021)A flexible, extensible software framework for model compression based on the LC algorithm., and . CoRR, (2020)Structured Multi-Hashing for Model Compression., , , , , , and . CVPR, page 11900-11909. Computer Vision Foundation / IEEE, (2020)Beyond Flops In Low-Rank Compression Of Neural Networks: Optimizing Device-Specific Inference Runtime., and . ICIP, page 2843-2847. IEEE, (2021)Model compression as constrained optimization, with application to neural nets. Part II: quantization., and . CoRR, (2017)