Fetching the paper…
Reading the bibliography…
We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning.
Backpropagation applied to handwritten zip code recognition
LeCun, Yann, Boser, Bernhard, Denker, John S, Henderson, Donnie, Howard, Richard E, Hubbard, Wayne, and Jackel, Lawrence D · 1989
Earlier work this paper cites.
On the best rank-1 approximation of higher-order supersymmetric tensors
Kofidis, Eleftherios and Regalia, Phillip A · 2002
Earlier work this paper cites.
High performance convolutional neural networks for document processing
Chellapilla, Kumar, Puri, Sidd, and Simard, Patrice · 2006
Earlier work this paper cites.
Tensor rank and the ill-posedness of the best low-rank approximation problem
De Silva, Vin and Lim, Lek-Heng · 2008
Earlier work this paper cites.
Tensor decompositions and applications
Kolda, T. G. and Bader, B. W · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
Earlier work this paper cites.
Subtracting a best rank-1 approximation may increase tensor rank
Stegeman, Alwin and Comon, Pierre · 2010
Earlier work this paper cites.
Large-scale FPGA-based convolutional networks
Farabet, Clément, LeCun, Yann, Kavukcuoglu, Koray, Culurciello, Eugenio, Martini, Berin, Akselrod, Polina, and Talay, Selcuk · 2011
Cited alongside, same era.
Large scale distributed deep networks
Dean, Jeffrey, Corrado, Greg, Monga, Rajat, Chen, Kai, Devin, Matthieu, Mao, Mark, Senior, Andrew, Tucker, Paul, Yang, Ke, Le, Quoc V, et al · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Cited alongside, same era.
Lin, Min, Chen, Qiang, and Yan, Shuicheng · 2013
Cited alongside, same era.
Fast training of convolutional networks through FFTs
Mathieu, Michael, Henaff, Mikael, and LeCun, Yann · 2013
Cited alongside, same era.
Exploiting linear structure within convolutional networks for efficient evaluation
Denton, Emily, Zaremba, Wojciech, Bruna, Joan, LeCun, Yann, and Fergus, Rob · 2014
Closest in time.
Deep features for text spotting
Jaderberg, Max, Vedaldi, Andrea, and Zisserman, Andrew · 2014
Closest in time.
Caffe: Convolutional architecture for fast feature embedding
Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, Jonathan, Girshick, Ross, Guadarrama, Sergio, and Darrell, Trevor · 2014
Closest in time.
Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2014
Closest in time.
Tensorlab v2.0
Sorber, L., Van Barel, M., and De Lathauwer, L · 2014
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning separable filters
Rigamonti, Roberto, Sironi, Amos, Lepetit, Vincent, and Fua, Pascal · 2013
Cited alongside, same era.
Convnet-benchmarks
Chintala, Soumith · 2014
Cited alongside, same era.
Speeding up convolutional neural networks with low rank expansions
Jaderberg, Max, Vedaldi, Andrea, and Zisserman, Andrew
Cited in the paper.
Deepface: Closing the gap to human-level performance in face verification
Taigman, Yaniv, Yang, Ming, Ranzato, Marc’Aurelio, and Wolf, Lior · 2014
Closest in time.
A comparison of algorithms for fitting the parafac model
Tomasi, Giorgio and Bro, Rasmus · 2014
Closest in time.