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Model compression is an essential technique for deploying deep neural networks (DNNs) on power and memory-constrained resources.
Distributed optimization and statistical learning via the alternating direction method of multipliers
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The link-prediction problem for social networks
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Distilling the knowledge in a neural network
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ImageNet Large Scale Visual Recognition Challenge
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Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., and Chen, Y · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Han, S., Mao, H., and Dally, W. J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Pruning filters for efficient convnets, 2016
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2016
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Structured pruning of deep convolutional neural networks
Anwar, S., Hwang, K., and Sung, W · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Channel pruning for accelerating very deep neural networks
He, Y., Zhang, X., and Sun, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Runtime neural pruning
Lin, J., Rao, Y., Lu, J., and Zhou, J · 2017
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Proximal policy optimization algorithms, 2017
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Structured probabilistic pruning for deep convolutional neural network acceleration
Wang, H., Zhang, Q., Wang, Y., and Hu, R · 2017
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Condensenet: An efficient densenet using learned group convolutions
Huang, G., Liu, S., Van der Maaten, L., and Weinberger, K. Q · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ma, N., Zhang, X., Zheng, H.-T., and Sun, J · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., and Welling, M · 2018
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Netadapt: Platform-aware neural network adaptation for mobile applications
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Hierarchical graph representation learning with differentiable pooling
Ying, R., You, J., Morris, C., Ren, X., Hamilton, W. L., and Leskovec, J · 2018
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NAT: Neural architecture transformer for accurate and compact architectures
Guo, Y., Zheng, Y., Tan, M., Chen, Q., Chen, J., Zhao, P., and Huang, J · 2019
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Hrank: Filter pruning using high-rank feature map
Lin, M., Ji, R., Wang, Y., Zhang, Y., Zhang, B., Tian, Y., and Shao, L · 2020
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AutoCompress: An automatic dnn structured pruning framework for ultra-high compression rates
Liu, N., Ma, X., Xu, Z., Wang, Y., Tang, J., and Ye, J · 2020
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Dicenet: Dimension-wise convolutions for efficient networks
Mehta, S., Hajishirzi, H., and Rastegari, M · 2020
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Scop: Scientific control for reliable neural network pruning
Tang, Y., Wang, Y., Xu, Y., Tao, D., XU, C., Xu, C., and Xu, C · 2020
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Apq: Joint search for network architecture, pruning and quantization policy
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Good subnetworks provably exist: Pruning via greedy forward selection
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He, Y., Liu, P., Wang, Z., Hu, Z., and Yang, Y · 2019
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Searching for mobilenetv3
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al · 2019
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Metapruning: Meta learning for automatic neural network channel pruning
Liu, Z., Mu, H., Zhang, X., Guo, Z., Yang, X., Cheng, K.-T., and Sun, J · 2019
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Bridging the gap between sample-based and one-shot neural architecture search with BONAS, 2019
Shi, H., Pi, R., Xu, H., Li, Z., Kwok, J. T., and Zhang, T · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
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Autoslim: Towards one-shot architecture search for channel numbers, 2019
Yu, J. and Huang, T · 2019
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Storage efficient and dynamic flexible runtime channel pruning via deep reinforcement learning
Chen, J., Chen, S., and Pan, S. J · 2020
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Ye, M., Gong, C., Nie, L., Zhou, D., Klivans, A., and Liu, Q · 2020
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Neuron-level structured pruning using polarization regularizer
Zhuang, T., Zhang, Z., Huang, Y., Zeng, X., Shuang, K., and Li, X · 2020
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Graph-based neural architecture search with operation embeddings
Chatzianastasis, M., Dasoulas, G., Siolas, G., and Vazirgiannis, M · 2021
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Only train once: A one-shot neural network training and pruning framework
Chen, T., Ji, B., Ding, T., Fang, B., Wang, G., Zhu, Z., Liang, L., Shi, Y., Yi, S., and Tu, X · 2021
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BRP-NAS: Prediction-based NAS using gcns, 2021
Dudziak, L., Chau, T., Abdelfattah, M. S., Lee, R., Kim, H., and Lane, N. D · 2021
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Network Pruning via Performance Maximization
Gao, S., Huang, F., Cai, W., and Huang, H · 2021
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A survey of quantization methods for efficient neural network inference, 2021
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., and Keutzer, K · 2021
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Parp: Prune, adjust and re-prune for self-supervised speech recognition
Lai, C.-I. J., Zhang, Y., Liu, A. H., Chang, S., Liao, Y.-L., Chuang, Y.-S., Qian, K., Khurana, S., Cox, D., and Glass, J · 2021
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Learnable Motion Coherence for Correspondence Pruning
Liu, Y., Liu, L., Lin, C., Dong, Z., and Wang, W · 2021
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Convolutional neural network pruning with structural redundancy reduction
Wang, Z., Li, C., and Wang, X · 2021
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Joint-detnas: Upgrade your detector with nas, pruning and dynamic distillation
Yao, L., Pi, R., Xu, H., Zhang, W., Li, Z., and Zhang, T · 2021
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Auto graph encoder-decoder for neural network pruning
Yu, S., Mazaheri, A., and Jannesari, A · 2021
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