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As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers.
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results, 2007
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
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Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Earlier work this paper cites.
Conditional computation in neural networks for faster models
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Courbariaux, M., Bengio, Y., and David, J.-P · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
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Lebedev, V., Ganin, Y., Rakhuba, M., Oseledets, I., and Lempitsky, V · 2015
Earlier work this paper cites.
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Ren, S., He, K., Girshick, R., and Sun, J · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q · 2016
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Binarized neural networks
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Deepx: A software accelerator for low-power deep learning inference on mobile devices
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Lin, Z., Courbariaux, M., Memisevic, R., and Bengio, Y · 2016
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Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q · 2017
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Dynamic computational time for visual attention
Li, Z., Yang, Y., Liu, X., Zhou, F., Wen, S., and Xu, W · 2017
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The Concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2017
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Changing model behavior at test-time using reinforcement learning
Odena, A., Lawson, D., and Olah, C · 2017
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Branchynet: Fast inference via early exiting from deep neural networks
Teerapittayanon, S., McDanel, B., and Kung, H · 2016
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Residual networks behave like ensembles of relatively shallow networks
Veit, A., Wilber, M. J., and Belongie, S · 2016
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A simple and fast implementation of faster r-cnn
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
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Wang, X., Yu, F., Dou, Z.-Y., and Gonzalez, J. E · 2017
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Parajuli, S., Raghavan, A., and Chai, S · 2018
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Efficient neural architecture search via parameter sharing
Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., and Dean, J · 2018
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Blockdrop: Dynamic inference paths in residual networks
Wu, Z., Nagarajan, T., Kumar, A., Rennie, S., Davis, L. S., Grauman, K., and Feris, R · 2018
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Aggregated residual transformations for deep neural networks
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