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The problem of quantizing the activations of a deep neural network is considered.
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Rectified linear units improve restricted boltzmann machines
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Improving neural networks by preventing co-adaptation of feature detectors
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Imagenet classification with deep convolutional neural networks
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Predicting parameters in deep learning
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Maxout networks
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On the difficulty of training recurrent neural networks
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On rectified linear units for speech processing
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Compressing deep convolutional networks using vector quantization
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Fitnets: Hints for thin deep nets
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Neural networks with few multiplications
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Faster R-CNN: towards real-time object detection with region proposal networks
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Imagenet large scale visual recognition challenge
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Going deeper with convolutions
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A unified multi-scale deep convolutional neural network for fast object detection
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M. Courbariaux, Y. Bengio, and J. David · 2015
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Fast R-CNN
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S. Han, H. Mao, and W. J. Dally · 2015
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Learning both weights and connections for efficient neural network
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Deep residual learning for image recognition
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size
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Overcoming challenges in fixed point training of deep convolutional networks
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Fixed point quantization of deep convolutional networks
D. D. Lin, S. S. Talathi, and V. S. Annapureddy · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
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