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Structured pruning is an effective approach for compressing large pre-trained neural networks without significantly affecting their performance.
An analysis of approximations for maximizing submodular set functions — I
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Submodular maximization in clean linear time, 2022
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Compressing deep convolutional networks using vector quantization
Y. Gong, L. Liu, M. Yang, and L. Bourdev · 2014
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Reshaping deep neural network for fast decoding by node-pruning
T. He, Y. Fan, Y. Qian, T. Tan, and K. Yu · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. V. Oseledets, and V. S. Lempitsky · 2015
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Diversity networks: Neural network compression using determinantal point processes
Z. Mariet and S. Sra · 2015
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Lazier than lazy greedy
B. Mirzasoleiman, A. Badanidiyuru, A. Karbasi, J. Vondrák, and A. Krause · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Data-free parameter pruning for deep neural networks
Optimal approximation for submodular and supermodular optimization with bounded curvature
M. Sviridenko, J. Vondrák, and J. Ward · 2017
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Combinatorial penalties: Structure preserved by convex relaxations
M. El Halabi, F. Bach, and V. Cevher · 2018
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Tensorial neural networks: Generalization of neural networks and application to model compression
J. Su, J. Li, B. Bhattacharjee, and F. Huang · 2018
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Discrimination-aware channel pruning for deep neural networks
Z. Zhuang, M. Tan, B. Zhuang, J. Liu, Y. Guo, Q. Wu, J. Huang, and J. Zhu · 2018
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Taxonomy and evaluation of structured compression of convolutional neural networks
A. Kuzmin, M. Nagel, S. Pitre, S. Pendyam, T. Blankevoort, and M. Welling · 2019
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S. Srinivas and R. V. Babu · 2015
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Restricted strong convexity implies weak submodularity
E. R. Elenberg, R. Khanna, A. G. Dimakis, and S. Negahban · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Guarantees for greedy maximization of non-submodular functions with applications
A. A. Bian, J. M. Buhmann, A. Krause, and S. Tschiatschek · 2017
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Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
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Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
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Thinet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
E. Voita, D. Talbot, F. Moiseev, R. Sennrich, and I. Titov · 2019
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What is the state of neural network pruning?
D. Blalock, J. J. G. Ortiz, J. Frankle, and J. Guttag · 2020
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Very fast streaming submodular function maximization, 2020
S. Buschjäger, P.-J. Honysz, and K. Morik · 2020
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Provable filter pruning for efficient neural networks
L. Liebenwein, C. Baykal, H. Lang, D. Feldman, and D. Rus · 2020
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The radicalization risks of gpt-3 and advanced neural language models
K. McGuffie and A. Newhouse · 2020
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Data-independent neural pruning via coresets
B. Mussay, M. Osadchy, V. Braverman, S. Zhou, and D. Feldman · 2020
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T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste · 2021
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Data-independent structured pruning of neural networks via coresets
B. Mussay, D. Feldman, S. Zhou, V. Braverman, and M. Osadchy · 2021
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huyvnphan/pytorch_cifar10, Jan. 2021
H. Phan · 2021
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