Fetching the paper…
Reading the bibliography…
At the core of any inference procedure in deep neural networks are dot product operations, which are the component that require the highest computational resources.
A. Rényi, “On measures of entropy and information,” in Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics , 1961, pp. 547–561
1961
Earlier work this paper cites.
I. S. Duff, “A survey of sparse matrix research,” Proceedings of the IEEE , vol. 65, no. 4, pp. 500–535, 1977
1977
Earlier work this paper cites.
D. M. Young and R. T. Gregory, A Survey of Numerical Mathematics . New York, NY, USA: Dover Publications, Inc., 1988
1988
Earlier work this paper cites.
Y. L. Cun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Advances in Neural Information Processing Systems 2 , 1990, pp. 598–605
1990
Earlier work this paper cites.
B. Hassibi, D. G. Stork, and G. J. Wolff, “Optimal brain surgeon and general network pruning,” in IEEE International Conference on Neural Networks , 1993, pp. 293–299 vol.1
1993
Earlier work this paper cites.
C. E. Shannon, “A mathematical theory of communication,” SIGMOBILE Mobile Computing and Communications Review , vol. 5, no. 1, pp. 3–55, 2001
2001
Earlier work this paper cites.
R. Yuster and U. Zwick, “Fast sparse matrix multiplication,” in Algorithms – ESA 2004 . Springer Berlin Heidelberg, 2004, pp. 604–615
2004
Earlier work this paper cites.
R. H. Landau, J. Paez, and C. C. Bordeianu, A Survey of Computational Physics: Introductory Computational Science . Princeton, NJ, USA: Princeton University Press, 2008
2008
Earlier work this paper cites.
V. Vanhoucke, A. Senior, and M. Z. Mao, “Improving the speed of neural networks on cpus,” in NIPS’11 Deep Learning and Unsupervised Feature Learning Workshop , 2011
2011
Earlier work this paper cites.
Y. LeCun, L. Bottou, G. B. Orr, and K. Müller, “Efficient backprop,” in Neural Networks: Tricks of the Trade - Second Edition, Springer LNCS 7700 , 2012, pp. 9–48
2012
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 25 , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
M. Bläser, “Fast matrix multiplication,” Theory of Computing, Graduate Surveys , vol. 5, pp. 1–60, 2013
2013
Earlier work this paper cites.
V. Karakasis, T. Gkountouvas, K. Kourtis, G. Goumas, and N. Koziris, “An extended compression format for the optimization of sparse matrix-vector multiplication,” IEEE Transactions on Parallel and Distributed Systems , vol. 24, no. 10, pp. 1930–1940, 2013
2013
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 , 2013, pp. 2148–2156
2013
Earlier work this paper cites.
S. Krig, Computer Vision Metrics: Survey, Taxonomy, and Analysis , 1st ed. Berkely, CA, USA: Apress, 2014
2014
Earlier work this paper cites.
P. Baldi, P. Sadowski, and D. Whiteson, “Searching for exotic particles in high-energy physics with deep learning,” Nature Communications , vol. 5, p. 4308, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Horowitz, “1.1 computing’s energy problem (and what we can do about it),” in IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) , 2014, pp. 10–14
2014
Cited alongside, same era.
Y. LeCun, Y. Bengio, and G. E. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Cited alongside, same era.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks , vol. 61, pp. 85–117, 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,” in IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 1026–1034
2015
Cited alongside, same era.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” in International Conference on Representation Learning (ICLR) , 2015
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, “EIE: efficient inference engine on compressed deep neural network,” in ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA) , 2016, pp. 243–254
2016
Later among the works it cites.
2016
Later among the works it cites.
K. T. Schütt, F. Arbabzadah, S. Chmiela, K. R. Müller, and A. Tkatchenko, “Quantum-chemical insights from deep tensor neural networks,” Nature Communications , vol. 8, p. 13890, 2017
2017
Later among the works it cites.
S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, and K.-R. Müller, “Machine learning of accurate energy-conserving molecular force fields,” Science Advances , vol. 3, no. 5, 2017
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…
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
S. Han, J. Pool, J. Tran, and W. J. Dally, “Learning both weights and connections for efficient neural networks,” in Advances in Neural Information Processing Systems , 2015, pp. 1135–1143
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016, http://www.deeplearningbook.org
2016
Cited alongside, same era.
S. Afroz, M. Tahaseen, F. Ahmed, K. S. Farshee, and M. N. Huda, “Survey on matrix multiplication algorithms,” in 5th International Conference on Informatics, Electronics and Vision (ICIEV) , 2016, pp. 151–155
2016
Cited alongside, same era.
J. King, T. Gilray, R. M. Kirby, and M. Might, “Dynamic-csr: A format for dynamic sparse-matrix updates,” in High Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings , ser. LNCS, vol. 9697. Springer Verlag, 2016, pp. 61–80
2016
Cited alongside, same era.
2016
Cited alongside, same era.
D. Molchanov, A. Ashukha, and D. Vetrov, “Variational dropout sparsifies deep neural networks,” in 34th International Conference on Machine Learning , 2017, pp. 2498–2507
2017
Later among the works it cites.
2017
Later among the works it cites.
K. Ullrich, E. Meeds, and M. Welling, “Soft Weight-Sharing for Neural Network Compression,” ArXiv e-prints , Feb. 2017
2017
Later among the works it cites.
C. Louizos, K. Ullrich, and M. Welling, “Bayesian Compression for Deep Learning,” in Advances in Neural Information Processing Systems , 2017, pp. 3290–3300
2017
Later among the works it cites.
M. Federici, K. Ullrich, and M. Welling, “Improved Bayesian Compression,” ArXiv e-prints , Nov. 2017
2017
Later among the works it cites.
T. Yang, Y. Chen, and V. Sze, “Designing energy-efficient convolutional neural networks using energy-aware pruning,” in IEEE International Conference on Computer Vision and Pattern Recognition , 2017, pp. 5687–5695
2017
Later among the works it cites.
Y. H. Chen, T. Krishna, J. S. Emer, and V. Sze, “Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks,” IEEE Journal of Solid-State Circuits , vol. 52, no. 1, pp. 127–138, 2017
2017
Later among the works it cites.
S. Bosse, D. Maniry, K.-R. Müller, T. Wiegand, and W. Samek, “Deep neural networks for no-reference and full-reference image quality assessment,” IEEE Transactions on Image Processing , vol. 27, no. 1, pp. 206–219, 2018
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
G. Montavon, W. Samek, and K.-R. Müller, “Methods for interpreting and understanding deep neural networks,” Digital Signal Processing , vol. 73, pp. 1–15, 2018
2018
Closest in time.
W. Samek, T. Wiegand, and K.-R. Müller, “Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models,” ITU Journal: ICT Discoveries - Special Issue 1 - The Impact of Artificial Intelligence (AI) on Communication Networks and Services , vol. 1, no. 1, pp. 39–48, 2018
2018
Closest in time.