Fetching the paper…
Reading the bibliography…
The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost.
Fast optimization methods for l1 regularization: A comparative study and two new approaches
M. Schmidt, G. Fung, and R. Rosales · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Torch7: A matlab-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning, 2011
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Maxout networks
I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
Earlier work this paper cites.
Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Network in network
M. Lin, Q. Chen, and S. Yan · 2014
Earlier work this paper cites.
Compressing neural networks with the hashing trick
W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Sparse convolutional neural networks
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, et al · 2015
Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2016
Later among the works it cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Later among the works it cites.
Group sparse regularization for deep neural networks
S. Scardapane, D. Comminiello, A. Hussain, and A. Uncini · 2016
Later among the works it cites.
Training sparse neural networks
S. Srinivas, A. Subramanya, and R. V. Babu · 2016
Later among the works it cites.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Binarynet: Training deep neural networks with weights and activations constrained to+ 1 or-1
M. Courbariaux and Y. Bengio · 2016
Cited alongside, same era.
Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger · 2016
Cited alongside, same era.
Neural network architecture optimization through submodularity and supermodularity
J. Jin, Z. Yan, K. Fu, N. Jiang, and C. Zhang · 2016
Cited alongside, same era.
Less is more: Towards compact cnns
H. Zhou, J. M. Alvarez, and F. Porikli · 2016
Later among the works it cites.
Designing neural network architectures using reinforcement learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 2017
Closest in time.
The power of sparsity in convolutional neural networks
S. Changpinyo, M. Sandler, and A. Zhmoginov · 2017
Closest in time.
Multi-scale dense convolutional networks for efficient prediction
G. Huang, D. Chen, T. Li, F. Wu, L. van der Maaten, and K. Q. Weinberger · 2017
Closest in time.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
Closest in time.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
Closest in time.