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Deep neural nets (DNNs) compression is crucial for adaptation to mobile devices.
Linear and quasi-linear equations of parabolic type , volume 23
O. Ladyženskaja, V. Solonnikov, and N. Ural’ceva · 1988
Earlier work this paper cites.
Model selection and estimation in regression with grouped variables
M. Yuan and Y. Lin · 2007
Earlier work this paper cites.
The split bregman method for l1-regularized problems
T. Goldstein and S. Osher · 2009
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, et al · 2011
Earlier work this paper cites.
Predicting parameters in deep learning
M. Denil, B. Shakibi, L. Dinh, M. Ranzato, and N. de Freitas · 2013
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: A Basic Course
Y. Nesterov · 2014
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
Earlier work this paper cites.
Sparse convolutional neural networks
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky · 2015
Earlier work this paper cites.
Data-free parameter pruning for deep neural networks
S. Srinivas and R. V. Babu · 2015
Earlier work this paper cites.
Learning the number of neurons in deep networks
Jose M Alvarez and Mathieu Salzmann · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
N. Carlini and D.A. Wagner · 2016
Earlier work this paper cites.
Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Cited alongside, same era.
A survey of model compression and acceleration for deep neural networks
Y. Cheng, D. Wang, P. Zhou, and T. Zhang · 2017
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
Later among the works it cites.
Deep learning for iot big data and streaming analytics: A survey
M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani · 2018
Later among the works it cites.
Cascade adversarial machine learning regularized with a unified embedding
T. Na, J. Ko, and S. Mukhopadhyay · 2018
Later among the works it cites.
Model compression via distillation and quantization
A. Polino, R. Pascanu, and D. Alistarh · 2018
Later among the works it cites.
Certifiable distributional robustness with principled adversarial training
A. Sinha, H. Namkoong, and J. Duchi · 2018
Later among the works it cites.
Adversarially trained model compression: When robustness meets efficiency
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Z. Li and Z. Shi · 2017
Cited alongside, same era.
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S. Yao, Y. Zhao, A. Zhang, L. Su, and T. Abdelzaher · 2017
Cited alongside, same era.
Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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Cited alongside, same era.
Neural ordinary differential equations
T. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud · 2018
Cited alongside, same era.
T. Dinh and J. Xin · 2018
Cited alongside, same era.
Sparse dnns with improved adversarial robustness
Y. Guo, C. Zhang, C. Zhang, and Y. Chen · 2018
Cited alongside, same era.
S. Gui, H. Wang, C. Yu, H. Yang, Z. Wang, and J. Liu · 2019
Later among the works it cites.
Y. Li, L. Li, L. Wang, T. Zhang, and B. Gong · 2019
Later among the works it cites.
Robust sparse regularization: Simultaneously optimizing neural network robustness and compactness
A. Rakin, Z. He, L. Yang, Y. Wang, L. Wang, and D. Fan · 2019
Later among the works it cites.
Channel pruning for deep neural networks via a relaxed group-wise splitting method
B. Yang, J. Lyu, S. Zhang, Y-Y Qi, and J. Xin · 2019
Later among the works it cites.
Second rethinking of network pruning in the adversarial setting
S. Ye, K. Xu, S. Liu, H. Cheng, J. Lambrechts, H. Zhang, A. Zhou, K. Ma, Y. Wang, and X. Lin · 2019
Later among the works it cites.
Blended coarse gradient descent for full quantization of deep neural networks
P. Yin, S. Zhang, J. Lyu, S. Osher, Y. Qi, and J. Xin · 2019
Later among the works it cites.
Theoretically principled trade-off between robustness and accuracy
H. Zhang, Y. Yu, J. Jiao, E. Xing, L. Ghaoui, and M. Jordan · 2019
Later among the works it cites.