Fetching the paper…
Reading the bibliography…
Convolutional neural networks (CNNs) have succeeded in many practical applications.
J. Sherman, W. J. Morrison, Adjustment of an inverse matrix corresponding to a change in one element of a given matrix, Annals of Mathematical Statistics 21 (1950) 124–127
1950
Earlier work this paper cites.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Backpropagation applied to handwritten zip code recognition, Neural Computation 1 (4) (1989) 541–551
1989
Earlier work this paper cites.
Y. LeCun, J. S. Denker, S. A. Solla, Optimal brain damage, in: D. S. Touretzky (Ed.), Advances in Neural Information Processing Systems 2, [NIPS Conference, Denver, Colorado, USA, November 27-30, 1989], Morgan Kaufmann, 1989, pp. 598–605
1989
Earlier work this paper cites.
doi:10.1109/5.726791
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11) (1998) 2278–2324 · 1998
Earlier work this paper cites.
N. Qian, On the momentum term in gradient descent learning algorithms, Neural Networks 12 (1) (1999) 145–151
1999
Earlier work this paper cites.
D. W. Blalock, J. J. G. Ortiz, J. Frankle, J. V. Guttag, What is the state of neural network pruning?, CoRR abs/2003.03033 · 2003
Earlier work this paper cites.
A. Krizhevsky, Learning multiple layers of features from tiny images, 2009
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, A. Ng, Reading digits in natural images with unsupervised feature learning, 2011
2011
Earlier work this paper cites.
U. Lotric, P. Bulic, Applicability of approximate multipliers in hardware neural networks, Neurocomputing 96 (2012) 57–65
2012
Earlier work this paper cites.
Tijmen Tieleman and Geoffrey Hinton, Rmsprop: Divide the gradient by a running average of its recent magnitude., https://amara.org/en/videos/vrXNiLBHyW92/en/180511/493899/ , coursera, 2012 (2012)
2012
Earlier work this paper cites.
D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, in: Y. Bengio, Y. LeCun (Eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015
2015
Cited alongside, same era.
S. Han, J. Pool, J. Tran, W. J. Dally, Learning both weights and connections for efficient neural network, in: C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, 2015, pp. 1135–1143
2015
Cited alongside, same era.
A. Blum, N. Haghtalab, A. D. Procaccia, Variational dropout and the local reparameterization trick, in: C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, 2015, pp. 2575–2583
2015
Cited alongside, same era.
Y. He, G. Kang, X. Dong, Y. Fu, Y. Yang, Soft filter pruning for accelerating deep convolutional neural networks, in: J. Lang (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, ijcai.org, 2018, pp. 2234–2240
2018
Later among the works it cites.
M. Á. Carreira-Perpiñán, Y. Idelbayev, "learning-compression" algorithms for neural net pruning, in: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, IEEE Computer Society, 2018, pp. 8532–8541
2018
Later among the works it cites.
R. Yu, A. Li, C. Chen, J. Lai, V. I. Morariu, X. Han, M. Gao, C. Lin, L. S. Davis, NISP: pruning networks using neuron importance score propagation, in: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, IEEE Computer Society, 2018, pp. 9194–9203
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society, 2016, pp. 770–778
2016
Cited alongside, same era.
S. Zhai, Y. Cheng, Z. M. Zhang, W. Lu, Doubly convolutional neural networks, in: D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, R. Garnett (Eds.), Advances in Neural Information Processing Systems, Vol. 29, Curran Associates, Inc., 2016
2016
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, R. B. Girshick, Mask R-CNN, in: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, IEEE Computer Society, 2017, pp. 2980–2988
2017
Cited alongside, same era.
doi:10.1145/3005348
S. Anwar, K. Hwang, W. Sung, J. V. Guttag, Structured pruning of deep convolutional neural networks, ACM J. Emerg. Technol. Comput. Syst. 13 (3) (2017) 32:1–32:18 · 2017
Cited alongside, same era.
H. Li, A. Kadav, I. Durdanovic, H. Samet, H. P. Graf, Pruning filters for efficient convnets, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, 2017
2017
Cited alongside, same era.
D. Molchanov, A. Ashukha, D. P. Vetrov, Variational dropout sparsifies deep neural networks, in: D. Precup, Y. W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, Vol. 70 of Proceedings of Machine Learning Research, PMLR, 2017, pp. 2498–2507
2017
Cited alongside, same era.
Y. Cheng, D. Wang, P. Zhou, T. Zhang, Model compression and acceleration for deep neural networks: The principles, progress, and challenges, IEEE Signal Processing Magazine 35 (1) (2018) 126–136
2018
Cited alongside, same era.
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. G. Howard, H. Adam, D. Kalenichenko, Quantization and training of neural networks for efficient integer-arithmetic-only inference, CoRR abs/1712.05877
Cited in the paper.
G. E. Hinton, O. Vinyals, J. Dean, Distilling the knowledge in a neural network, CoRR abs/1503.02531
Cited in the paper.
M. Kang, B. Han, Operation-aware soft channel pruning using differentiable masks, in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Vol. 119 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 5122–5131
2020
Later among the works it cites.
X. Ma, F. Guo, W. Niu, X. Lin, J. Tang, K. Ma, B. Ren, Y. Wang, PCONV: the missing but desirable sparsity in DNN weight pruning for real-time execution on mobile devices, in: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, AAAI Press, 2020, pp. 5117–5124
2020
Later among the works it cites.
M. Li, D. Ding, A. Heldring, J. Hu, R. Chen, G. Vecchi, Low-rank matrix factorization method for multiscale simulations: A review, IEEE Open Journal of Antennas and Propagation 2 (2021) 286–301
2021
Later among the works it cites.
L. Zhang, K. Ma, Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors, in: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, 2021
2021
Later among the works it cites.
P. Hu, X. Peng, H. Zhu, M. M. S. Aly, J. Lin, OPQ: compressing deep neural networks with one-shot pruning-quantization, in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, AAAI Press, 2021, pp. 7780–7788
2021
Later among the works it cites.
C. Zhang, Q. Song, H. Zhou, Y. Ou, H. Deng, L. T. Yang, Revisiting recursive least squares for training deep neural networks (2021) · 2021
Later among the works it cites.