Fetching the paper…
Reading the bibliography…
Compressing giant neural networks has gained much attention for their extensive applications on edge devices such as cellphones.
R. Burbidge, M. Trotter, B. Buxton, and S. Holden, “Drug design by machine learning: support vector machines for pharmaceutical data analysis,” Computers & chemistry , vol. 26, no. 1, pp. 5–14, 2001
2001
Earlier work this paper cites.
V. Koltchinskii, D. Panchenko et al. , “Empirical margin distributions and bounding the generalization error of combined classifiers,” The Annals of Statistics , vol. 30, no. 1, pp. 1–50, 2002
2002
Earlier work this paper cites.
J. Goldberger, S. Gordon, and H. Greenspan, “An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures,” in null . IEEE, 2003, p. 487
2003
Earlier work this paper cites.
J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, “Covariate shift and local learning by distribution matching,” 2008
2008
Earlier work this paper cites.
A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM journal on imaging sciences , vol. 2, no. 1, pp. 183–202, 2009
2009
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
V. Vanhoucke, A. Senior, and M. Z. Mao, “Improving the speed of neural networks on cpus,” 2011
2011
Earlier work this paper cites.
A. Coates, A. Y. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in AISTATS , ser. JMLR Proceedings, vol. 15. JMLR.org, 2011, pp. 215–223
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
M. Denil, B. Shakibi, L. Dinh, M. Ranzato, and N. de Freitas, “Predicting parameters in deep learning,” in Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States. , 2013, pp. 2148–2156. [Online]. Available: http://papers.nips.cc/paper/5025-predicting-parameters-in-deep-learning
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: common objects in context,” in ECCV (5) , ser. Lecture Notes in Computer Science, vol. 8693. Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep neural networks with binary weights during propagations,” in Advances in neural information processing systems , 2015, pp. 3123–3131
2015
Earlier work this paper cites.
S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” in Advances in neural information processing systems , 2015, pp. 1135–1143
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV) , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
D. J. Rezende, S. Mohamed, I. Danihelka, K. Gregor, and D. Wierstra, “One-shot generalization in deep generative models,” in ICML , ser. JMLR Workshop and Conference Proceedings, vol. 48. JMLR.org, 2016, pp. 1521–1529
2016
Earlier work this paper cites.
L. Maaloe, C. K. Sonderby, S. K. Sonderby, and O. Winther, “Auxiliary deep generative models,” in ICML , ser. JMLR Workshop and Conference Proceedings, vol. 48. JMLR.org, 2016, pp. 1445–1453
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks,” in Advances in neural information processing systems , 2016, pp. 4107–4115
2016
Cited alongside, same era.
X. Zhang, J. Zou, K. He, and J. Sun, “Accelerating very deep convolutional networks for classification and detection,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 10, pp. 1943–1955, 2016
2016
Cited alongside, same era.
J. M. Alvarez and M. Salzmann, “Learning the number of neurons in deep networks,” in Advances in Neural Information Processing Systems , 2016, pp. 2270–2278
2016
Cited alongside, same era.
Y. He, X. Zhang, and J. Sun, “Channel pruning for accelerating very deep neural networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1389–1397
2017
Later among the works it cites.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network.” in CVPR , vol. 2, no. 3, 2017, p. 4
2017
Later among the works it cites.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Computer Vision and Pattern Recognition (CVPR) , vol. 1, no. 2, 2017, p. 4
2017
Later among the works it cites.
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “Xnor-net: Imagenet classification using binary convolutional neural networks,” in European Conference on Computer Vision . Springer, 2016, pp. 525–542
2016
Cited alongside, same era.
F. Li, B. Zhang, and B. Liu, “Ternary weight networks,” arXiv preprint arXiv:1605.04711 , 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Y. Guo, A. Yao, and Y. Chen, “Dynamic network surgery for efficient dnns,” in Advances in Neural Information Processing Systems 29 , D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 1379–1387. [Online]. Available: http://papers.nips.cc/paper/6165-dynamic-network-surgery-for-efficient-dnns.pdf
2016
Cited alongside, same era.
V. Lebedev and V. S. Lempitsky, “Fast convnets using group-wise brain damage,” in CVPR . IEEE Computer Society, 2016, pp. 2554–2564
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. Qiao, Z. Zhang, W. Shen, B. Wang, and A. L. Yuille, “Gradually updated neural networks for large-scale image recognition,” in ICML , ser. JMLR Workshop and Conference Proceedings, vol. 80. JMLR.org, 2018, pp. 4185–4194
2018
Later among the works it cites.
H. Wen, K. Han, J. Shi, Y. Zhang, E. Culurciello, and Z. Liu, “Deep predictive coding network for object recognition,” in ICML , ser. JMLR Workshop and Conference Proceedings, vol. 80. JMLR.org, 2018, pp. 5263–5272
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Wang, C. Xu, C. Xu, and D. Tao, “Adversarial learning of portable student networks,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
B. Reagen, U. Gupta, B. Adolf, M. Mitzenmacher, A. M. Rush, G. Wei, and D. Brooks, “Weightless: Lossy weight encoding for deep neural network compression,” in ICML , ser. JMLR Workshop and Conference Proceedings, vol. 80. JMLR.org, 2018, pp. 4321–4330
2018
Later among the works it cites.
M. A. Carreira-Perpinán and Y. Idelbayev, “Learning-compression algorithms for neural net pruning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8532–8541
2018
Later among the works it cites.
Y. Wang, C. Xu, C. Xu, and D. Tao, “Packing convolutional neural networks in the frequency domain,” IEEE transactions on pattern analysis and machine intelligence , 2018
2018
Later among the works it cites.
J. Cheng, P. Wang, G. Li, Q. Hu, and H. Lu, “Recent advances in efficient computation of deep convolutional neural networks,” Frontiers of IT & EE , vol. 19, no. 1, pp. 64–77, 2018
2018
Later among the works it cites.
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, “Exploring the limits of weakly supervised pretraining,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 181–196
2018
Later among the works it cites.
J. Cheng, P.-s. Wang, G. Li, Q.-h. Hu, and H.-q. Lu, “Recent advances in efficient computation of deep convolutional neural networks,” Frontiers of Information Technology & Electronic Engineering , vol. 19, no. 1, pp. 64–77, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
J.-H. Luo, H. Zhang, H.-Y. Zhou, C.-W. Xie, J. Wu, and W. Lin, “Thinet: pruning cnn filters for a thinner net,” IEEE transactions on pattern analysis and machine intelligence , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
H. Chang, J. Lu, F. Yu, and A. Finkelstein, “Pairedcyclegan: Asymmetric style transfer for applying and removing makeup,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
P. Molchanov, A. Mallya, S. Tyree, I. Frosio, and J. Kautz, “Importance estimation for neural network pruning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 264–11 272
2019
Closest in time.
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning structured sparsity in deep neural networks,” in Advances in Neural Information Processing Systems , 2016, pp. 2074–2082
2082
Closest in time.