Fetching the paper…
Reading the bibliography…
Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs.
Spert-ii: A vector microprocessor system
J. Wawrzynek, K. Asanovic, B. Kingsbury, D. Johnson, J. Beck, and N. Morgan · 1996
Earlier work this paper cites.
Optimization of collective communication operations in mpich
R. Thakur, R. Rabenseifner, and W. Gropp · 2005
Earlier work this paper cites.
Benchmarking GPUs to tune dense linear algebra
V. Volkov and J. W. Demmel · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
MDB: A memory-mapped database and backend for openldap
H. Chu · 2011
Earlier work this paper cites.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
B. Recht, C. Re, S. Wright, and F. Niu · 2011
Earlier work this paper cites.
Conversational speech transcription using context-dependent deep neural networks
F. Seide, G. Li, and D. Yu · 2011
Earlier work this paper cites.
Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, Q. V. Le, and A. Y. Ng · 2012
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.
Approaching exascale: application requirements for olcf leadership computing
V. Anantharaj, F. Foertter, W. Joubert, and J. Wells · 2013
Earlier work this paper cites.
Communication-minimizing 2d convolution in gpu registers
F. N. Iandola, D. Sheffield, M. Anderson, P. M. Phothilimthana, and K. Keutzer · 2013
Earlier work this paper cites.
M. Lin, Q. Chen, and S. Yan · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean · 2013
Earlier work this paper cites.
Multi-gpu training of convnets
O. Yadan, K. Adams, Y. Taigman, and M. Ranzato · 2013
Earlier work this paper cites.
FireBox: a hardware building block for 2020 warehouse-scale computers
K. Asanovic and D. Patterson · 2014
Earlier work this paper cites.
cuDNN: efficient primitives for deep learning
S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer · 2014
Earlier work this paper cites.
Project Adam: building an efficient and scalable deep learning training system
T. Chilimbi, Y. Suzue, J. Apacible, and K. Kalyanaraman · 2014
Cited alongside, same era.
Keynote: Large scale deep learning
J. Dean · 2014
Cited alongside, same era.
Theano-based large-scale visual recognition with multiple gpus
W. Ding, R. Wang, F. Mao, and G. W. Taylor · 2014
Cited alongside, same era.
Deep speech: Scaling up end-to-end speech recognition
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates, and A. Y. Ng · 2014
Cited alongside, same era.
Densenet: Implementing efficient convnet descriptor pyramids
F. N. Iandola, M. W. Moskewicz, S. Karayev, R. B. Girshick, T. Darrell, and K. Keutzer · 2014
Cited alongside, same era.
From generic to specific deep representations for visual recognition
H. Azizpour, A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson · 2015
Closest in time.
The effects of hyperparameters on SGD training of neural networks
T. M. Breuel · 2015
Closest in time.
Keynote: Large scale deep learning
J. Dean · 2015
Closest in time.
From captions to visual concepts and back
H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Dollar, J. Gao, X. He, M. Mitchell, J. C. Platt, C. L. Zitnick, and G. Zweig · 2015
Closest in time.
Real-time, content-driven representations at twitter
C. Farabet · 2015
Closest in time.
Deformable part models are convolutional neural networks
R. B. Girshick, F. N. Iandola, T. Darrell, and J. Malik · 2015
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
One weird trick for parallelizing convolutional neural networks
A. Krizhevsky · 2014
Cited alongside, same era.
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
F. Seide, H. Fu, J. Droppo, G. Li, and D. Yu · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Cited alongside, same era.
Fast convolutional nets with fbfft: A gpu performance evaluation
N. Vasilache, J. Johnson, M. Mathieu, S. Chintala, S. Piantino, and Y. LeCun · 2014
Cited alongside, same era.
BVLC googlenet
S. Guadarrama · 2015
Closest in time.
A deep neural network compression pipeline: Pruning, quantization, huffman encoding
S. Han, H. Mao, and W. J. Dally · 2015
Closest in time.
DeepLogo: Hitting logo recognition with the deep neural network hammer
F. N. Iandola, A. Shen, P. Gao, and K. Keutzer · 2015
Closest in time.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Closest in time.
maxDNN: an efficient convolution kernel for deep learning with maxwell gpus
A. Lavin · 2015
Closest in time.
End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2015
Closest in time.
Large scale distributed deep learning on hadoop clusters
C. Noel, J. Shi, and A. Feng · 2015
Closest in time.
Introducing titan: Advancing the era of accelerated computing
Oak Ridge Leadership Computing Facility (OLCF) · 2015
Closest in time.
Scalable distributed dnn training using commodity gpu cloud computing
N. Strom · 2015
Closest in time.
Deep image: Scaling up image recognition
R. Wu, S. Yan, Y. Shan, Q. Dang, and G. Sun · 2015
Closest in time.
Empirical evaluation of rectified activations in convolutional network
B. Xu, N. Wang, T. Chen, and M. Li · 2015
Closest in time.