Retrain-free fully connected layer optimization using matrix factorization
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[1] Yurong Chen,et al. Dynamic Network Surgery for Efficient DNNs , 2016, NIPS.
[2] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[3] Kevin Skadron,et al. Scalable parallel programming with CUDA , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).
[4] H. Sebastian Seung,et al. Algorithms for Non-negative Matrix Factorization , 2000, NIPS.
[5] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[6] Babak Hassibi,et al. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.
[7] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[8] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[9] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[10] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[11] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[12] P. Paatero,et al. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .
[13] Misha Denil,et al. Predicting Parameters in Deep Learning , 2014 .
[14] Chris H. Q. Ding,et al. Convex and Semi-Nonnegative Matrix Factorizations , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Lorien Y. Pratt,et al. Comparing Biases for Minimal Network Construction with Back-Propagation , 1988, NIPS.
[16] Ebru Arisoy,et al. Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[17] Jian Sun,et al. Efficient and accurate approximations of nonlinear convolutional networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] John Tran,et al. cuDNN: Efficient Primitives for Deep Learning , 2014, ArXiv.
[19] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[20] Joan Bruna,et al. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation , 2014, NIPS.