A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off

NeurIPS 2019 Yaniv BlumenfeldDar GilboaDaniel Soudry

Reducing the precision of weights and activation functions in neural network training, with minimal impact on performance, is essential for the deployment of these models in resource-constrained environments. We apply mean-field techniques to networks with quantized activations in order to evaluate the degree to which quantization degrades signal propagation at initialization... (read more)

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