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Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity.
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Backpropagation for energy-efficient neuromorphic computing
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Very deep convolutional networks for large-scale image recognition
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Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification
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Binarized neural networks
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Proximal mean-field for neural network quantization
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Forward and backward information retention for accurate binary neural networks
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