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We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks.
Approximation by superpositions of a sigmoidal function
Cybenko, G.: · 1989
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Comparing biases for minimal network construction with back-propagation
Hanson, S.J., Pratt, L.Y.: · 1989
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Optimal brain damage
LeCun, Y., Denker, J.S., Solla, S.A., Howard, R.E., Jackel, L.D.: · 1989
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Second order derivatives for network pruning: Optimal brain surgeon
Hassibi, B., Stork, D.G.: · 1993
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Conversational speech transcription using context-dependent deep neural networks
Seide, F., Li, G., Yu, D.: · 2011
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Improving the speed of neural networks on cpus
Vanhoucke, V., Senior, A., Mao, M.Z.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Oculus rift-virtual reality headset for 3d gaming
Oculus, V.: · 2012
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Predicting parameters in deep learning
Denil, M., Shakibi, B., Dinh, L., de Freitas, N., et al.: · 2013
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Big neural networks waste capacity
Dauphin, Y.N., Bengio, Y.: · 2013
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Lin, M., Chen, Q., Yan, S.: · 2013
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Provable bounds for learning some deep representations
Arora, S., Bhaskara, A., Ge, R., Ma, T.: · 2013
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Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., Cun, Y.L., Fergus, R.: · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
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Do deep nets really need to be deep?
Ba, J., Caruana, R.: · 2014
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Exploiting linear structure within convolutional networks for efficient evaluation
Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y., Fergus, R.: · 2014
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Speeding up convolutional neural networks with low rank expansions
Jaderberg, M., Vedaldi, A., Zisserman, A.: · 2014
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Compressing deep convolutional networks using vector quantization
Gong, Y., Liu, L., Yang, M., Bourdev, L.: · 2014
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Fixed-point feedforward deep neural network design using weights+ 1, 0, and- 1
Hwang, K., Sung, W.: · 2014
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Cross-domain synthesis of medical images using efficient location-sensitive deep network
Van Nguyen, H., Zhou, K., Vemulapalli, R.: · 2015
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Han, S., Mao, H., Dally, W.J.: · 2015
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Compressing neural networks with the hashing trick
Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: · 2015
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Fixed point optimization of deep convolutional neural networks for object recognition
Anwar, S., Hwang, K., Sung, W.: · 2015
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Neural networks with few multiplications
Lin, Z., Courbariaux, M., Memisevic, R., Bengio, Y.: · 2015
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Courbariaux, M., Bengio, Y., David, J.P.: · 2014
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Expectation backpropagation: parameter-free training of multilayer neural networks with continuous or discrete weights
Soudry, D., Hubara, I., Meir, R.: · 2014
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Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Fast r-cnn
Girshick, R.: · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Backpropagation for energy-efficient neuromorphic computing
Esser, S.K., Appuswamy, R., Merolla, P., Arthur, J.V., Modha, D.S.: · 2015
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Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M., Bengio, Y., David, J.P.: · 2015
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Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses
Baldassi, C., Ingrosso, A., Lucibello, C., Saglietti, L., Zecchina, R.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
Courbariaux, M., Bengio, Y.: · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V.: · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 1mb model size
Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: · 2016
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Kim, M., Smaragdis, P.: · 2016
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Darknet: Open source neural networks in c
Redmon, J.: · 2016
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